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The anatomy of A.L.I.C.E
Abstract
This paper is a technical presentation of Artificial Linguistic Internet Computer Entity (A.L.I.C.E.) and Artificial Intelligence
Markup Language (AIML), set in context by historical and philosophical ruminations on human consciousness. A.L.I.C.E., the
first AIML-based personality program, won the Loebner Prize as “the most human computer” at the annual Turing Test contests
in 2000, 2001, and 2004. The program, and the organization that develops it, is a product of the world of free software. More
than 500 volunteers from around the world have contributed to her development. This paper describes the history of A.L.I.C.E.
and AIML-free software since 1995, noting that the theme and strategy of deception and pretense upon which AIML is based can
be traced through the history of Artificial Intelligence research. This paper goes on to show how to use AIML to create robot
personalities like A.L.I.C.E. that pretend to be intelligent and selfaware. The paper winds up with a survey of some of the
philosophical literature on the question of consciousness. We consider Searle’s Chinese Room, and the view that natural language
understanding by a computer is impossible. We note that the proposition “consciousness is an illusion” may be undermined by
the paradoxes it apparently implies. We conclude that A.L.I.C.E. does pass the Turing Test, at least, to paraphrase Abraham
Lincoln, for some of the people some of the time.
KeywordsArtificial Intelligence-natural language-chat robot-bot-Artificial Intelligence Markup Language (AIML)-Markup Languages-XML-HTML-philosophy of mind-consciousness-dualism-behaviorism-recursion-stimulusresponse-Turing Test-Loebner Prize-free software-open source-A.L.I.C.E-Artificial Linguistic Internet Computer Entity-deception-targeting
... Another step forward in the history of chatbots was the creation, in 1995, of ALICE (Artificial Linguistic Internet Computer Entity), the first online chatbot inspired by ELIZA (Wallace, 2009). ALICE was based on pattern-matching, without any actual perception of the whole conversation (Marietto et al., 2013) but with a discussion ability on the web that allowed longitude and included any topic. ...
... All categories are stored in an object called Graphmaster, which has the form of a tree with its nodes representing the categories and its leaves representing the templates that are the chatbot responses. AIML uses a pattern matching technique that performs a first depth search in the Graphmaster to match the best pattern (Wallace, 2009). ...
This literature review presents the History, Technology, and Applications of Natural Dialog Systems or simply chatbots. It aims to organize critical information that is a necessary background for further research activity in the field of chatbots. More specifically, while giving the historical evolution, from the generative idea to the present day, we point out possible weaknesses of each stage. After we present a complete categorization system, we analyze the two essential implementation technologies, namely, the pattern matching approach and machine learning. Moreover, we compose a general architectural design that gathers critical details, and we highlight crucial issues to take into account before system design. Furthermore, we present chatbots applications and industrial use cases while we point out the risks of using chatbots and suggest ways to mitigate them. Finally, we conclude by stating our view regarding the direction of technology so that chatbots will become really smart.
... Chatbots are a special kind of information system that uses artificial intelligence technologies to provide a natural language user interface. Since the first applications of conversational information systems, e.g., ELIZA (Weizenbaum, 1966) or ALICE (Wallace, 2009), different approaches were pursued. Independent of the technological advancements of the last years and the use of different synonyms, e.g., chatbot, chatterbot, conversational agent, or digital agent, the main characteristics have not changed (Dale, 2016). ...
... For several years, chatbot research has been on the rise as many researchers address the topic from different perspectives. As mentioned, first instantiations were pursued years ago by (Weizenbaum, 1966) and (Wallace, 2009). Since these early prototypes, different approaches have been undertaken that focus mainly on the application areas by design research or through some kind of meta-or application area-independent research. ...
Background: Chatbots are currently on the rise as more and more researchers tackle this topic from different perspectives. Simultaneously, workplaces and ways of working are increasingly changing in the context of digitalization. However, despite the promised benefits, the changes still show problems that should be tackled more purposefully by chatbots. Application areas and underlying objectives of a chatbot application at digital workplaces especially have not been researched yet.
Method: To solve the existing problems and close the research gap, we did a qualitative empirical study based on the grounded-theory process. Therefore, we interviewed 29 experts in a cross-section of different industry sectors and sizes. The experts work in the information systems domain or have profound knowledge of (future) workplace design, especially regarding chatbots.
Results: We identified three fundamental usage scenarios of chatbots in seven possible application areas. As a result of this, we found both divisional and cross-divisional application areas at workplaces. Furthermore, we detected fifteen underlying objectives of a chatbot operation, which can be categorized from direct over mid-level to indirect ones. We show dependencies between them, as well.
Conclusions: Our results prove the applicability of chatbots in workplace settings. The chatbot operation seems especially fruitful in the support or the self-service domain, where it provides information, carries out processes, or captures process-related data. Additionally, automation, workload reduction, and cost reduction are the fundamental objectives of chatbots in workplace scenarios. With this study, we contribute to the scientific knowledge base by providing knowledge from practice for future research approaches and closing the outlined research gap.
... Conversational agents are systems that mimic human conversation through the exploitation of technologies such as Natural Language Processing, Artificial Intelligence, and Voice Recognition [14]. At first, Conversational agent research aimed at creating an intelligence to be indistinguishable from humans and pass the Turing Test [15], as in the case of ELIZA [16], ALICE [17], and PARRY [18]. Thanks to the advancement of the underlying technologies, in recent years, Conversational Technology became extremely popular in the research panorama. ...
... Several dialogue interfaces translate a conversation into SQL(-like) languages; some examples are Microsoft English, Precise, and Athena [32][33][34]. In the bioinformatics domain, BioGraphBot [35] is a Conversational Agent that exploits the ALICE framework [17], allowing users to retrieve data from BioGraphDB, a publicly available graph database based on the Gremlin query language [36]. To use this application, though, the user must have some knowledge of the query language and be aware of the underlying schema. ...
With the availability of reliable and low-cost DNA sequencing, human genomics is relevant to a growing number of end-users, including biologists and clinicians. Typical interactions require applying comparative data analysis to huge repositories of genomic information for building new knowledge, taking advantage of the latest findings in applied genomics for healthcare. Powerful technology for data extraction and analysis is available, but broad use of the technology is hampered by the complexity of accessing such methods and tools.
This work presents GeCoAgent, a big-data service for clinicians and biologists. GeCoAgent uses a dialogic interface, animated by a chatbot, for supporting the end-users’ interaction with computational tools accompanied by multi-modal support. While the dialogue progresses, the user is accompanied in extracting the relevant data from repositories and then performing data analysis, which often requires the use of statistical methods or machine learning. Results are returned using simple representations (spreadsheets and graphics), while at the end of a session the dialogue is summarized in textual format. The innovation presented in this article is concerned with not only the delivery of a new tool but also our novel approach to conversational technologies, potentially extensible to other healthcare domains or to general data science.
... It is an annual competition for chatbots where they are tested based on Turing Test. ALICE [81] has won this prize 3 times and Mitsuku [86] has won this prize 5 times including in 2019. ...
... • Scalability: It should accept number of questions from user and respond efficiently. [81] has won this prize 3 times as well as Mitsuku [86] has won it for 5 times. • Interoperability: It is the ability of a system to exchange and reuse information it supports multiple channels and users can switch between channels. ...
A chatbot is emerged as an effective tool to address the user queries in automated, most appropriate and accurate way. Depending upon the complexity of the subject domain, researchers are employing variety of soft-computing techniques to make the chatbot user-friendly. It is observed that chatbots have flooded the globe with wide range of services including ordering foods, suggesting products, advising for insurance policies, providing customer support, giving financial assistance, schedule meetings etc. However, public administration based services wherein chatbot intervention influence the most, is not explored yet. This paper discuses about artificial intelligence based chatbots including their applications, challenges, architecture and models. It also talks about evolution of chatbots starting from Turing Test and Rule-based chatbots to advanced Artificial Intelligence based Chatbots (AI-Chatbots). AI-Chatbots are providing much kind of services, which this paper outlines into two main aspects including customer based services and public administration based services. The purpose of this survey is to understand and explore the possibility of customer & public administration services based chatbot. The survey demonstrates that there exist an immense potential in the AI assisted chatbot system for providing customer services and providing better governance in public administration services.
... Since the earliest chatbot, Eliza, was developed in 1966 (Weizenbaum, 1966), many chatbots have been designed and developed (Colby, 1975;Shieber, 1994). The chatbot Alice, developed by Richard S. Wallace in 1995, was once considered the most intelligent chatbot (Wallace, 2009;. In recent years, with breakthroughs in applications of deep learning, speech recognition, and pattern recognition technology in the field of natural language processing, chatbot technologies have been experiencing a surge. ...
With the rapid development of artificial intelligence (AI), countries are increasingly adopting AI-guided chatbots to improve service on government portals. The reduction in face-to-face services under COVID-19 pandemic will further accelerate this trend. However, the adoption and performance of the existing chatbots differ. Based on the literature on e-government adoption and innovative policy innovation diffusion, this article examines both the initial and postadoption stages of chatbot usage in China’s local governments. While the first phase employs the survival model of event history analysis to explore the factors that influence the decision to adopt chatbots in local government, the second phase analyzes the determinants of those chatbots’ performance in the postadoption stage. We find that pressure factors and readiness factors play different roles in the different adoption stages. Although pressure can encourage local governments to implement chatbots, these governments’ readiness determined how well the chatbots perform after their initial adoption. The implications and limitations of the research are also discussed.
... It uses handwritten templates to generate responses that are similar to what the user says. Since then, numerous hand-coded, chatbots have been developed [23]. In addition, a number of programming frameworks specifically designed to facilitate the creation of dialog agents have been developed [13]. ...
A conversational agent (chatbot) is a software that can communicate with humans using natural language. Conversation modeling is an extremely important topic in natural language processing and artificial intelligence (AI). Indeed, since the birth of AI, creating a good chatbot remains one of the most difficult challenges in this field. Although chatbots can be used for a variety of tasks, they generally need to understand what users are saying and to provide appropriate answers to their questions. In this paper, we present midoBot: a deep learning Arabic chatbot based on the seq2seq model. midoBot is capable of conversing with humans on popular conversation topics through text. We built the model and tested it in the Tensorflow 2 deep learning framework using the most seq 2 seq Model architectures. We use a dataset of ~81,659 pairs of conversations created manually and without any handcrafted rules. Our algorithm was trained on a VM on google cloud (GPU TESLA K80 10 GO). The results obtained are significant, In most questions the chatbot was able to reproduce good answers.
... While in 2013, WeChat launched its chatbot platform. In sum, in these years, the idea of creating a machine capable of interacting with humans through language, which dates back to Turing's imitation game (Turing, 1950) and the earlier efforts in conversational software, such as ELIZA (Weizenbaum, 1966), PARRY (Colby et al., 1971), and ALICE (Wallace, 2009), became far more popular among the general population. ...
Over the last ten years there has been a growing interest around text-based chatbots, software applications interacting with humans using natural written language. However, despite the enthusiastic market predictions, ‘conversing’ with this kind of agents seems to raise issues that go beyond their current technological limitations, directly involving the human side of interaction. By adopting a Human-Computer Interaction (HCI) lens, in this article we present a systematic literature review of 83 papers that focus on how users interact with text-based chatbots. We map the relevant themes that are recurrent in the last ten years of research, describing how people experience the chatbot in terms of satisfaction, engagement, and trust, whether and why they accept and use this technology, how they are emotionally involved, what kinds of downsides can be observed in human-chatbot conversations, and how the chatbot is perceived in terms of its humanness. On the basis of these findings, we highlight open issues in current research and propose a number of research opportunities that could be tackled in future years.
... ELIZA (Weizenbaum, 1966) and A.L.I.C.E. (Wallace, 2009) are examples of early chatbot technologies, where the main goal was to mimic human conversations. Over the years, the chatbot concept has evolved. ...
Chatbots’ growing popularity has brought new challenges to HCI, having changed the patterns of human interactions with computers. The increasing need to approximate conversational interaction styles raises expectations for chatbots to present social behaviors that are habitual in human–human communication. In this survey, we argue that chatbots should be enriched with social characteristics that cohere with users’ expectations, ultimately avoiding frustration and dissatisfaction. We bring together the literature on disembodied, text-based chatbots to derive a conceptual model of social characteristics for chatbots. We analyzed 56 papers from various domains to understand how social characteristics can benefit human–chatbot interactions and identify the challenges and strategies to designing them. Additionally, we discussed how characteristics may influence one another. Our results provide relevant opportunities to both researchers and designers to advance human–chatbot interactions.
... The concept itself is not new but the advance of computer technologies and the situational opportunity have pushed these types of programs further. Machine learning proved to be a great combination with Artificial Intelligence Markup Language [2] invented by Richard S. Wallace. The language's data storing format provides a good foundation for building encoders and decoders to manipulate the text patterns that need to be processed with a machine learning algorithm and readable after. ...
... For many decades, the development of social bots, or intelligent dialogue systems that can engage in empathetic conversations with humans, is one of the main goals of Artificial Intelligence. As stated in [38], early conversational systems such as Eliza [39], Parry [40], and Alice [41] were designed to mimic human behaviour in a text-based conversation, hence to pass the Turing Test [42] within a controlled scope. Despite their impressive successes, these systems, which were precursors to today's social chatbots, worked well only in constrained environments. ...
The article proposes a system for knowledge-based conversation designed for Social Robots and other conversational agents. The proposed system relies on an Ontology for the description of all concepts that may be relevant conversation topics, as well as their mutual relationships. The article focuses on the algorithm for Dialogue Management that selects the most appropriate conversation topic depending on the user's input. Moreover, it discusses strategies to ensure a conversation flow that captures, as more coherently as possible, the user's intention to drive the conversation in specific directions while avoiding purely reactive responses to what the user says. To measure the quality of the conversation, the article reports the tests performed with 100 recruited participants, comparing five conversational agents: (i) an agent addressing dialogue flow management based only on the detection of keywords in the speech, (ii) an agent based both on the detection of keywords and the Content Classification feature of Google Cloud Natural Language, (iii) an agent that picks conversation topics randomly, (iv) a human pretending to be a chatbot, and (v) one of the most famous chatbots worldwide: Replika. The subjective perception of the participants is measured both with the SASSI (Subjective Assessment of Speech System Interfaces) tool, as well as with a custom survey for measuring the subjective perception of coherence.
... Conversational agents, commonly known as chatbots, are programs that can interact using natural language [2]. Conceptualized by Alan Turing [49], chatbots have evolved from early keyword matching implementations (e.g., ELIZA [51]), to pattern matching agents built using the Artificial Intelligence Markup Language (e.g., ALICE [50]), to complex knowledge-based models trained on corpora comprising billions of words (e.g., Google's Meena [4]). Nowadays, chatbots come in a variety of architectures [11] and permeate almost every domain of human-computer interaction (HCI) [14,30,48]. ...
Chatbots have long been advocated for computer-assisted language learning systems to support learners with conversational practice. A particular challenge in such systems is explaining mistakes stemming from ambiguous grammatical constructs. Misplaced modifiers, for instance, do not make sentences ungrammatical, but introduce ambiguity through the misplacement of an adverb or prepositional phrase. In certain cases, the ambiguity gives rise to humor, which can serve to illustrate the mistake itself. We conducted an online experiment with 400 native English speakers to explore the use of a chatbot to harness such humor. In an interaction resembling an advanced grammar exercise, the chatbot presented participants with a phrase containing a misplaced modifier, explained the ambiguity in the phrase, acknowledged (or ignored) the humor that the ambiguity gave rise to, and suggested a correction. Participants then completed a questionnaire, rating the chatbot with respect to ten traits. A quantitative analysis showed a significant increase in how participants rated the chatbot's personality, humor, and friendliness when it acknowledged the humor arising from the misplaced modifier. This effect was observed whether the acknowledgment was conveyed using verbal, nonverbal (emoji), or mixed cues.
... In contrast, generation-based techniques try to generate the output wordby-word, based on probability distributions. For both creation patterns a variety of techniques were proposed: Statistical approaches based on Markov Chain Models (Hutchens & Alder, 1998;Levin et al., 2000), ontology utilization for identifying related concepts, pattern matching and a new markup language, the Artificial Intelligence Mark-up Language (AIML; Abdul-Kader & Woods, 2015; Wallace, 2007). While originally, the main goal of chat bots was to pass the Turing test, the functionality of those programs was utilized in many different domains ranging from social interaction such as flirting or joking (Augello et al., 2008), also for teaching (Kerly et al., 2007;Shaw, 2012) and to supporting people with certain neurological diseases like Parkinson's (Ireland et al., 2015). ...
Recently, social bots, (semi-) automatized accounts in social media, gained global attention in the context of public opinion manipulation. Dystopian scenarios like the malicious amplification of topics, the spreading of disinformation, and the manipulation of elections through “opinion machines” created headlines around the globe. As a consequence, much research effort has been put into the classification and detection of social bots. Yet, it is still unclear how easy an average online media user can purchase social bots, which platforms they target, where they originate from, and how sophisticated these bots are. This work provides a much needed new perspective on these questions. By providing insights into the markets of social bots in the clearnet and darknet as well as an exhaustive analysis of freely available software tools for automation during the last decade, we shed light on the availability and capabilities of automated profiles in social media platforms. Our results confirm the increasing importance of social bot technology but also uncover an as yet unknown discrepancy of theoretical and practically achieved artificial intelligence in social bots: while literature reports on a high degree of intelligence for chat bots and assumes the same for social bots, the observed degree of intelligence in social bot implementations is limited. In fact, the overwhelming majority of available services and software are of supportive nature and merely provide modules of automation instead of fully fledged “intelligent” social bots.
... The impact of conversational ITS s can be augmented by the use of animated agents [7]. For instance, among the most popular chatbot technologies are ALICE [29] and ChatScript [24]. ...
In this work we present an Intelligent Pedagogic Agent (IPA) we developed to act within the Gea2: A New Earth educational game. The IPA was designed and developed based on a pilot study, where information was gathered from students and teachers of four classes of two high school institutions. The IPA has the ability to be conversational in Natural Language, and is also capable of autonomous intervention through the use of unsolicited hints during learner’s gameplay. The autonomous intervention is based on both the detection of player’s emotions, and the state of player’s game.
... In 1995, Richard S. Wallace built A.L.I.C.E. [4] a chatbot made entirely with open source software that uses the AIML language, child of the XML language from which it inherits extensibility, which thus allows the chatbot to hold a conversation. With the growing interest in arti icial intelligence and with the idea of simplifying the interaction between man and machine, more and more companies, have developed or directed part of their research on chatbots. ...
The configuration and management of devices and applications in Internet of Things (IoT) platforms may be very complicated for a user, which may limit the usage of relevant functionalities and which does not allow its full potential to be exploited. To address this issue, in this paper we present a new chatbot which is intended to assist the user in interacting with an IoT platform and allow them to use and exploit its full potential. The requirements for a user-centric design of the chatbot are first analyzed, then a proper solution is designed which exploits a serverless approach and makes extensive use of Artificial Intelligence (AI) tools. The developed chatbot is integrated with Telegram to message between the user and the Lysis IoT platform. The performance of the developed chatbot is analyzed to assess its effectiveness when accessing the platform, set the main devices' parameters and request data of interest.
... Task-oriented agents are designed for a particular task and are set up to have short conversations, usually within a closed domain such as online shopping, customer support, or medical expertise. Many techniques can be adopted to build this type of agent, such as parsing Weizenbaum (1966), pattern matching Wallace (2009), and more recently with the use of neural networks (Nuez Ezquerra 2018; Csaky 2019). The approach we adopt is rule-based and relies on deep learning classification, which gives the agent the ability to respond to a given message with the purpose of facilitating argumentative discussions. ...
Online discussion platforms are perceived as the next-generation method of citizen involvement. Such platforms can collect, integrate, and synthesize opinions to achieve social good. Crowd-scale platforms are being developed and deployed in social experiments that involve citizens and local governments. In such platforms, human facilitation is often used to preserve the quality of the discussions. Human facilitators often face difficulties when the discussions grow in size. In this paper, we present “D-agree,” a crowd-scale discussion support system based on an automated facilitation agent. The agent extracts discussion structures from online discussions, analyzes them, and posts facilitation messages. We conducted small- and large-scale social experiments in Japan to assess the social impact of the platform. The results showcase the success of our automated facilitation agents in gathering valuable opinions from citizens. In addition, our experiments highlight the effect of an automated facilitation agent on online discussions. In particular, we find that combining the agent facilitator with human facilitators leads to higher user satisfaction.
... Inspired by ELIZA and earned the Loebner prize in January 2000. (Fryer and Carpenter, 2006;Wallace, 2009) The learning aspects rely on supervised learning, as the individual teaches and tracks the robot's conversations and suggests new AIML content to make the responses more relevant, sensible, precise, and believable. As ALICE is a predefined set of questions and answers, the robustness to answer every query is still missing. ...
Conversational AI intends for machine-human interactions to appear and feel more natural and inclined to communicate in a near-human context. Chatbots, also known as conversational agents, are typically divided into two types of use-cases: task-oriented bots and social friend-bots. Task-oriented bots are often used to do activities such as answering questions or solving basic queries. Furthermore, social-friend-bots are designed to communicate like humans, where the user can speak freely and the bot answers organically while maintaining the conversation’s ambience. This paper analyses recent works in the conversational AI domain examining the exclusive methodologies, existing frameworks or tools, evaluation metrics, and available datasets for building robust conversational agents. Finally, a mind-map encompassing all the stated elements and qualities of chatbots is created.
... For many decades, the development of social bots, or intelligent dialogue systems that can engage in empathetic conversations with humans, has been one of the main goals of Artificial Intelligence. As stated in [38], early conversational systems such as Eliza [39], Parry [40], and Alice [41] have been designed to mimic human behaviour in a text-based conversation, hence to pass the Turing Test [42] within a controlled scope. Despite their impressive successes, these systems, which were precursors to today's social chatbots, worked well only in constrained environments. ...
The article proposes a system for knowledge-based conversation designed for Social Robots and other conversational agents. The proposed system relies on an Ontology for the description of all concepts that may be relevant conversation topics, as well as their mutual relationships. The article focuses on the algorithm for Dialogue Management that selects the most appropriate conversation topic depending on the user input. Moreover, it discusses strategies to ensure a conversation flow that captures, as more coherently as possible , the user intention to drive the conversation in specific directions while avoiding purely reactive responses to what the user says. To measure the quality of the conversation, the article reports the tests performed with 100 recruited participants, comparing five conversational agents: (i) an agent addressing dialogue flow management based only on the detection of keywords in the speech, (ii) an agent based both on the detection of keywords and the Content Classification feature of Google Cloud Natural Language, (iii) an agent that picks conversation topics randomly, (iv) a human pretending to be a chatbot, and (v) one of the most famous chatbots worldwide: Replika. The subjective perception of the participants is measured both with the SASSI (Subjective Assessment of Speech System Interfaces) tool, as well as with a custom survey for measuring the subjective perception of coherence.
... Automating advice, education, or therapy is not a new goal. There is a long history of attempts to create autonomous systems, starting with "Eliza" (Weizenbaum, 1966), and more recent attempts such as "A.L.I.C.E" (Wallace, 2009) and Sophia who were created by Hanson Robotics in 2016 (Retto, 2017). Automation is defined as "applications of robotics, artificial intelligence, machine learning, machine vision, and similar emerging and mature digital technologies that will allow human work to be substituted by computer capital" (Willis et al., 2019, p. 2). ...
Introduction: Avatars are becoming more common in virtual reality, used as a guide, teacher, companion, or mentor through immersive experiences. Special attention needs to be paid to their design to ensure credibility and working alliance, to allow for the optimal delivery of behavior change content.
Methods: We present a new embodied Semi-Autonomous Mentoring Intelligence (SAMI) avatar used in an immersive virtual reality intervention for the self-management of chronic pain. We discuss the research findings that were taken into consideration and guided the design and development of SAMI, such methods to promote working alliance with non-human agents, optimal characteristics of non-human agents, and features of effective “automation”.
Conclusion: We provide a table of considerations and recommendations for researchers involved in designing future virtual reality characters. We provide suggestions on how future research could advance SAMI further for use in pain management and related interventions.
... Representative systems that use such rules are ELIZA (Weizenbaum 1966) and A.L.I.C.E. (Wallace 2009). ...
In our commercial chat-oriented dialogue system, we have been using an utterance database created from a massive amount of predicate-argument structures extracted from the web for generating utterances. However, because the creation of this database involves several automated processes, the database often includes non-sentences (ungrammatical or uninterpretable sentences) and utterances with inappropriate topic information (called off-focus utterances). Additionally, utterances tend to be monotonous and uninformative because they are created from single predicate-argument structures. To resolve these problems, we propose methods for filtering non-sentences by using neural network-based methods and utterances inappropriate for their associated foci by using co-occurrence statistics. To reduce monotony, we also propose a method for concatenating automatically generated utterances so that the utterances can be longer and richer in content. Experimental results indicate that the non-sentence filter can successfully remove non-sentences with an accuracy of 95% and that our focus filter can filter utterances inappropriate for their foci with high recall. We also examine the effectiveness of our filtering methods and concatenation method through an experiment involving human participants. The experimental results indicate that our methods significantly outperform a baseline in terms of understandability and that the concatenation of two utterances leads to higher familiarity and content richness while retaining understandability.
... Cleverbot was introduced in 2008, and unlike previous chatbots, its responses were not preprogrammed. Instead, it learned directly from human input, such as when a user typed a comment or query, and Cleverbot searched for all keywords or an exact phrase that matches the input [24]. ...
Studies on the impact of emotions on learning have focused primarily on using and understanding emotions close to joy and happiness to improve human experiences. However, little is said about apparently emotions, such as disgust, anger, sadness and fear and their uses and benefits within the academic teaching environment. In this study, we used a chatbot as a companion to give feedback that would evoke negative emotions. The study produced promising results for enhancing learning processes using negative emotions.
... Parry has a rule-based structure that is similar to Eliza but with a better understanding capability, controlling structure, and simulating emotions. Alice, "Artificial Linguistic Internet Computer Entity", allows its users to customize their chatbots by using an "Artificial Intelligence Markup Language" (Wallace, 2009). Although Alice can maintain a dialogue for a limited period of time, it was awarded as the most human-like system. ...
Artificial intelligence (AI)-based chatbots have received considerable attention during the last few years. However, little is known concerning what affects their use for educational purposes. This research, therefore, develops a theoretical model based on extracting constructs from the expectation confirmation model (ECM) (expectation confirmation, perceived usefulness, and satisfaction), combined with the knowledge management (KM) factors (knowledge sharing, knowledge acquisition, and knowledge application) to understand the sustainable use of chatbots. The developed model was then tested based on data collected through an online survey from 448 university students who used chatbots for learning purposes. Contrary to the prior literature that mainly relied on structural equation modeling (SEM) techniques, the empirical data were analyzed using a hybrid SEM-artificial neural network (SEM-ANN) approach. The hypotheses testing results reinforced all the suggested hypotheses in the developed model. The sensitivity analysis results revealed that knowledge application has the most considerable effect on the sustainable use of chatbots with 96.9% normalized importance, followed by perceived usefulness (70.7%), knowledge acquisition (69.3%), satisfaction (61%), and knowledge sharing (19.6%). Deriving from these results, the study highlighted a number of practical implications that benefit developers, designers, service providers, and instructors.
... I was surprised to see that it knew slang words and also words from non-English languages like Hindi.text-to-speech capabilities, Dr. Sbaitso was similar to ELIZA in that it used pattern-matching techniques to respond with questions such as 'How are you feeling?' and 'Why do you feel that way?'15 . No complex interactions such as contextual conversations were possible because the bot did not have capabilities such as advanced intent detection or context awareness; neither could it learn over time, like PARRY.The year 1995 marked the creation of ALICE (Artificial Linguistic Internet Computer Entity) by Richard Wallace, a chatbot that was capable of simulating conversations to imitate a human girl16 . ALICE was a universal language-processing chatbot. ...
Inspired by Alan Turing's 1950 'Turing Test', researchers and technology companies have attempted to build conversational interfaces that interact with humans using natural language in a realistic way. Today's chatbot technologies have evolved considerably since their inception along many dimensions, but typically interact with only a single human in dyadic communication. Here, I study chatbots that interact with multiple stakeholders (e.g., members of a family, team, or organization) to carry out a shared task. In particular, I identify new opportunities to build chatbots that aid and improve human social interactions. To accomplish this, I begin by surveying state-of- the-art of chatbot technologies, reviewing their architecture and capabilities. I then consider empirical evidence about how people spend their time and all the ways that people interact with each other. I then carry out three case studies of particular social interactions: maintenance of shared households, caretaking, and peer-to-peer selling. For each domain of interaction, I identify the component activities that make up the interaction and find synergies and opportunities for multi-
party chatbots to improve the quality of those interactions.
... A conversational bot [14] is software that simulates a conversation with a person by providing automatic responses to user input. In their early days, in the 1980s and 1990s, the first conversational bots, such as Eliza [15], A.L.I.C.E [16] were able to carry on a conversation by means of pre-prepared responses. Today, a distinction is made between bots that follow basic rules, similar to the first bots, and are therefore not very intelligent, and bots that are based on artificial intelligence. ...
In the current health crisis due to COVID-19, people with intellectual disabilities have especially suffered. The development of their social abilities has been restricted, first with the lockdown and then with the current limitation of social life. They have lost some of these abilities or are having difficulty practicing them. CapacitaBOT, our use case, is a mobile application based on a chatbot, which allows people with intellectual disabilities to work and train their social skills. A chatbot is a software tool that allows to maintain a conversation in automatic way between the user and the machine, the mobile application. CapacitaBOT can be considered by its features, an educational ICT tool that introduces innovation, inclusion and quality in order to be integrated into education for people with intellectual disabilities. The tool trains these people for real-life situations and can also be considered a resource that allows the application of active methodologies since it makes easy the learning of social skills. In addition, all the contributions of the tool are aligned with the objectives of sustainable development because it is a tool that facilities the accessibility of people with disabilities, who more than ever have been affected by social isolation caused by the COVID-19 crisis.
... Early systems such as ELIZA (Weizenbaum,2 Our cleaned datasets and source code are available at: https://github.com/yq-wen/ overlapping-datasets 1966) and ALICE (Wallace, 2009) are based on manually crafted rules. Recent advances in deep neural networks have allowed dialogue systems to be trained end-to-end using massive dialogue corpora (Shang et al., 2015;Li et al., 2016;Serban et al., 2016). ...
Open-domain dialogue systems aim to converse with humans through text, and its research has heavily relied on benchmark datasets. In this work, we first identify the overlapping problem in DailyDialog and OpenSubtitles, two popular open-domain dialogue benchmark datasets. Our systematic analysis then shows that such overlapping can be exploited to obtain fake state-of-the-art performance. Finally, we address this issue by cleaning these datasets and setting up a proper data processing procedure for future research.
... Wallace [23] created this XML dialect. A.L.I.C.E [24] was the first chatbot based on AIML. The learning model used in ALICE is supervised one, i.e. it is being supervised by a person, the botmaster. ...
Amazon’s Alexa, Apple’s Siri, Google Assistant and Microsoft’s Cortana, clearly illustrate the impressive research work and potentials to be explored in the field of conversational agents. Conversational agent, chatter-bot or chatbot is a program expected to converse with near-human intelligence. Chatbots are designed to be used either as task-oriented ones or simply open-ended dialogue generator. Many approaches have been proposed in this field which ranges from earlier versions of hard-coded response generator to the advanced development techniques in Artificial Intelligence. In a broader sense, these can be categorized as rule-based and neural network based. While rule-based relies on predefined templates and responses, a neural network based relies on deep learning models. Rule-based are preferable for simpler task-oriented conversations. Open-domain conversational modeling is a more challenging area and uses mostly neural network-based approaches. This paper begins with an introduction of chatbots, followed by in-depth discussion on various classical or rule-based and neural-network-based approaches. The evaluation metrics employed for chatbots are mentioned. The paper concludes with a table consisting of recent research done in the field. It covers all the latest and significant publications in the field, the evaluation metrics employed, the corpus which is used as well as the possible areas of enhancement that exist in the proposed techniques.
... Pattern based will use user pattern input to determine the response. In early days, many patterns based chatbot was researched using handcrafted pattern [20][21] [22]. While using pattern based chatbot is handy, static rule cannot solve the high number of user input variance especially if user have some typos or use synonyms and different terms to state their intention. ...
Nowadays, chatbots have become more and more prominent in various domains. Nevertheless, designing a versatile chatbot, giving reasonable answers, is a challenging task. Thereby, the major drawback of most chatbots is their limited scope. Multi-agent-based systems offer approaches to solve problems in a cooperative manner following the “divide and conquer” paradigm. Consequently, it seems promising to design a multi-agent-based chatbot approach scaling beyond the scope of a single application context. To address this research gap, we propose a novel approach orchestrating well-established conversational assistants. We demonstrate and evaluate our approach using six chatbots, providing higher quality than competing artifacts.
Chatbot is an AI based application that stimulates human conversations. In this research, we proposed a noble approach to the design of Chatbot that interacts with people in Hausa Language, which is the second most famous languages in Africa. Numerous chatbots have been created and continue to be created. To the best of our knowledge, there is very limited or no Chatbot that target Hausa Language, hence the catalyst of this research. The proposed Chatbot is capable of communicating with users in Hausa Language, tell some funny stories and above all has the ability of learning from users. It was designed as a web-based application that can feature in any platform with internet access and a web browser, where PHP was used as programming Language in conjunction with MySQL database. The interface was designed using HTML and CSS with JavaScript for interactivity. Developing this Chatbot system would bring the Hausa language into the Artificial intelligence stage and it would serve as a reference material for future research and development. The result shows that the system is capable of communicating in simple Hausa words and will be able to learn simple words in Hausa. Developing system whose ability is to learn, adapt and perform autonomously can be a major field of competition between technology inventors and Hausa-based language processing over the next few years.
The virtual bot is one of the hot topics in artificial intelligence, where most of the current studies focus on chatbots. Nevertheless, the context-sensitive virtual bot, especially with rich human-like interactions (e.g., appearance change, context-aware narration/recommendation) regarding the ambient changes (e.g., location, focused scene) through various built-in sensors, would have broader application. Towards this direction, we propose MateBot, a human-like, context-sensitive virtual bot, which supports harmonious human-computer interaction on smartphones. The design of MateBot consists of three parts. First, a context sensing network is used to recognize the input background information and face information, and modify the appearance of the virtual bot through the conversion of the encoding network. Second, a human-like bot appearance generation network can generate a virtual bot image with a human-like appearance through the GAN network and modify the appearance of the virtual bot with context-sensitive information. Third, a personalized conversation network is devised to communicate with human users. Furthermore, we apply MateBot to the intelligent travel scenario to justify its practicality, and the experiment results show that the bot can better increase the user’s sense of substitution and improve the communication efficiency between human users and virtual bots.
A chatbot is an application that simulates human conversation. It is an artificial intelligence (AI) feature which could be deployed across verticals such as business, education, and medical. Chatbots are virtual assistant which can assist the users on how to do something. Students in schools and colleges expect instant answers to questions or problems. Nowadays, classes, exams, and graduation are conducted online, and it is essential to have chatbots in college/university that could solve issues during the admission process. Students need not come to college nor searching for expertise to answer their queries. They can just have a conversation with chatbot and get necessary information. Chatbots can give immediate response for queries asked by students and it is accessible 24/7. Moreover, students may find difficulties in choosing the right course with their previous education. Therefore, in this paper, we present the chatbot—Stera—that is designed to provide information about hostels, college campus, career guidance training, courses, GPA for particular semester, and much more. Students might have lot of queries regarding courses and college activities, and without a chatbot, college/university cannot give instant reply to each and every student. The main drive of creating an AI-powered Stera chatbot for college/university is to provide the needed information for students without delay in response.
A very rare case!!!we did operate a patient with a giant stone on an intra uterine dispositive prolapse in vagina. complicated with a vesico-vaginal fistula. visible on gynecological examination. we did not find a huger stone in literature . Have a good reading
Recognition of human activities is a challenging process and technology-based systems are used for the successful realization of the process. Recently, artificial intelligence-based technologies have started to be used widely. The hybrid approach designed for this study consists of convolutional-based bidirectional long short-term memory (CBiLSTM). In this study, 12 types of human activities were identified using C-BiLSTM using ECG signal data. As a result of the analysis, an overall accuracy success rate of 98.96% was achieved. The result obtained in the experimental analysis has been promising in identifying the types of human activity.
This chapter describes the latest developments in Conversational Intelligent Tutoring Systems (CITS), which are e-learning systems that adopt Computational Intelligence approaches to mimic a human tutor. CITS deliver a personalized, human-like tutorial via natural language. Like expert human tutors, CITS automatically profile a learner’s knowledge and skills and also profile and adapt to an individual’s affective traits (such as mood or personality) to improve their learning. The chapter explores the challenges for CITS design that prevent them from moving into the mainstream and highlights scalability issues that the research community must address. Two cutting-edge CITS are described. Oscar CITS dynamically profiles learning styles using learner behaviour throughout the tutorial conversation, and adapts at a question level, resulting in significantly better learning gain. Hendrix 2.0 CITS uses a bank of artificial neural networks to automatically profile a learner’s comprehension using webcam images, and intervenes to help improve learner motivation and avoid impasse. The considerable issue of trust in AI is discussed, and a study of public opinion on ethical use of AI in education showed that more needs to be done in educating the public on the benefits and risks of AI in education.
The objective of the paper is to analyze the algorithms and methods used in dialogue chatbot systems in terms of usability and quality of their functioning. The research used two knowledge corpuses differing in subject matter, number of specific intents, availability of entities, and number of training and test data. The research was performed on three platforms - RASA, Dialogflow, and IBM Watson. The influence of the number of intents and the presence of entities on the quality of the dialogue system using predefined metrics was examined. Additionally, the experiments concerned the analysis of chatbot operation for different ratios of training to test data. Moreover, the influence of the platform and corpus selection on the quality of operation and acceptance level of intent was examined.
University websites and online portals are the primary means through which potential students and other stakeholders find important information about an institution. University websites are essential to these organizations’ marketing and communication efforts. In this paper, focus has been put on the need to complement these websites with the use of an AI Chatbot (UniBot) in order to serve more efficiently. This study aims at performing an extensive literature survey on intelligent conversational agents and the feasibility of applying them in enhancing online communication in universities. The study utilizes an iterative – incremental methodology to aid in design and development of UniBot, using AIML (Artificial Intelligent Markup Language) Pattern matching algorithm on the Pandorabot (AIAAS) platform, to generate high quality training data, with which, the agents Natural Language Understanding (NLU) model is trained. The study also provides for training and testing the agent using data which is acquired from Online Communication, University Website department at Kenyatta University.
With the rise of Artificial Intelligence over the last years, virtual assistants, so-called bots, are becoming more relevant than ever before. Especially the technology triggers a growing interest in numerous fields of application. Technologies like machine learning enables the integration of emotion management and thus provides the opportunity to enhance interaction with the user. Hereby, apprenticeship as a contextual research setting proves to be a particularly promising use case. Our paper examines the potentials and challenges of emotionally sensitive applications in a training environment. The results of a qualitative approach demonstrate extensive benefits especially in the individualization of the learning process. Hereby, gamification can represent an interesting aspect in this context. However, fears emerge that the technical implementation may not sufficiently cover pedagogical backgrounds and that the applications may encounter low acceptance.
A chat-oriented dialogue system can become more likeable if it can remember information about users and use that information during a dialogue. We propose a chat-oriented dialogue system that can use user information acquired during a dialogue and discuss its effectiveness on long-term interaction. In our subjective evaluation over five consecutive days, we compared three systems: a system that can remember and use user information over multiple days (proposed system), one that can only remember user information within a single dialogue session, and another that does not remember any user information. We found that users were significantly more satisfied with our proposed system than with the other two. This paper is the first to verify the effectiveness of remembering on long-term interaction with a fully automated chat-oriented dialogue system.
The field of education has experienced a transformation as artificial intelligence (AI) becomes increasingly applicable for learning purposes. AI has the potential to transform the social interactions in educational contexts among learners, teachers, and technologies. In this systematic mapping review, we focus on mapping and framing trends for educational applications of AI in simulation-based learning. Fifty-nine studies met the inclusion and exclusion criteria. We coded and analyzed six mapped categories in this literature review: (1) the year-of-study trend, (2) methods, (3) AI technologies, (4) simulation, (5) study trends, and (6) learning principles and theories. To provide nuanced details from the included literature, we also synthesized three thematic trends: (1) AI built in virtual agents for simulation-based learning, (2) AI infused in simulation-based learning with affective computing, and (3) AI leveraged in simulation-based learning for assessments. Trend One builds on a general acknowledgement of virtual agents as a guide for situated learning. Trend Two posits the role of affective states in learning trajectories and suggests the related machine learning approaches. Trend Three discusses machine learning techniques and multimodal computing used for assessment and feedback. The paper concludes with implications and suggestions for research and practice in AI in education using simulation-based learning.
Artificial Intelligence and machine learning in machines is a very challenging discussion. It involves the creation of machines which can stimulate intelligence and
adapt to the needs of the human kind. This paper discusses some of the current trends and practices in A.I. and subsequently offers alternative theory for improvement of
some of today’s prominent postulates. For this, focus on the structuring and functioning of simple - chatbots( or chatter bots) is made. The paper shows how the current
approach towards A.I. and M.L. is not adequate and offers a new path that discusses machine learning, machine intelligence, python programming throwing light to the
future of Intelligent systems.
We tackle the question to what extent artificial intelligence can have an emotional component, and to what extent it can resemble a human. The article’s main aim is to provide an overview of artificial intelligence systems containing emotional intelligence elements and show their development directions. Several innovative projects were analysed that attempted to apply emotional intelligence to an artificial intelligence algorithm. The article presents the emotional component in the currently used artificial intelligence in everyday life solutions. Potential threats associated with the development of emotional intelligence in humanoids and the importance of this development for societies are also discussed.
In recent years, communication robots aiming to offer mental support to the elderly have attracted increasing attention. Dialogue systems consisting of two robots could provide the elderly with opportunities to hold longer conversations in care homes. In this study, we conducted an experiment to compare two types of scenario-based dialogue systems with different types of bodies—physical and virtual robots—to investigate the effects of embodying such dialogue systems. Forty elderly people aged from 65 to 84 interacted with either an embodied desktop-sized humanoid robot or computer graphic agent displayed on a monitor. The elderly participants were divided into groups depending on the success of the interactions. The results revealed that (i) in the group where the robots responded more successfully with the expected conversation flow, the elderly are more engaged in the conversation with the physical robots than the virtual robots, and (ii) the elderly in the group in which robots responded successfully are more engaged in the conversation with the physical robots than those in the group in which the robots responded with ambiguous responses owing to unexpected utterances from the elderly. These results suggest that having a physical body is advantageous in promoting high engagement, and the potential advantage appears depending on whether the system can handle the conversation flow. These findings provide new insight into the development of dialogue systems assisting elderly in maintaining a better mental health.
Chatbots have been used in many fields ranging from education to healthcare and are also used in e-commerce settings. This research aims at developing a web-based chatbot called Hebron for the Covenant University Community Mall. The chatbot is developed using Python and React.js as the programming languages and MySQL (Structured Query Language) server as the database to give a structure to the e-commerce datasets and Admin Portal process. The e-commerce chatbot application for Covenant University Shopping Mall (CUSM) seeks to provide an easy, smart, and comfortable shopping experience for the Covenant University Community.
This paper concerns the problem of realizing consistent personalities in neural conversational modeling by using user generated question-answer pairs as training data. Using the framework of role play-based question-answering, we collected single-turn question-answer pairs for particular characters from online users. Meta information was also collected such as emotion and intimacy related to question-answer pairs. We verified the quality of the collected data and, by subjective evaluation, we also verified their usefulness in training neural conversational models for generating responses reflecting the meta information, especially emotion.
The use of chatbots evolved rapidly in numerous fields in recent years, including Marketing, Supporting Systems, Education, Health Care, Cultural Heritage, and Entertainment. In this paper, we first present a historical overview of the evolution of the international community’s interest in chatbots. Next, we discuss the motivations that drive the use of chatbots, and we clarify chatbots’ usefulness in a variety of areas. Moreover, we highlight the impact of social stereotypes on chatbots design. After clarifying necessary technological concepts, we move on to a chatbot classification based on various criteria, such as the area of knowledge they refer to, the need they serve and others. Furthermore, we present the general architecture of modern chatbots while also mentioning the main platforms for their creation. Our engagement with the subject so far, reassures us of the prospects of chatbots and encourages us to study them in greater extent and depth.
Case-based reasoning is a recent approach to problem solving and learning that has got a lot of attention over the last few years. Originating in the US, the basic idea and underlying theories have spread to other continents, and we are now within a period of highly active research in case-based reasoning in Europe as well. This paper gives an overview of the foundational issues related to case-based reasoning, describes some of the leading methodological approaches within the field, and exemplifies the current state through pointers to some systems. Initially, a general framework is defined, to which the subsequent descriptions and discussions will refer. The framework is influenced by recent methodologies for knowledge level descriptions of intelligent systems. The methods for case retrieval, reuse, solution testing, and learning are summarized, and their actual realization is discussed in the light of a few example systems that represent different CBR approaches. We also discuss the role of case-based methods as one type of reasoning and learning method within an integrated system architecture.
I had some strong reactions to Joe Weizenbaum's book, Computer Power and Human Reason. The book mentions some important concerns which are obscured by harsh and sometimes shrill accusations against the Artificial Intelligence research community. On the whole, it seems to me that the personal attacks distract and mislead the reader from more valuable abstract points. I strongly recommend Samuel Florman's article "In Praise of Technology" in the November, 1975, issue of Harper's Magazine to see a different opinion about the role of technology in modern society.
"In this book I have tried to explain what the science of psychology is like and how it got that way." Emphasis is on key topics, presented in historical order. The more technological and applied aspects are omitted. Interwoven with discussions of traditional experimental areas are biographies of Wundt, William James, Galton, Pavlov, Freud, and Binet. A glossary and suggestions for further reading are appended. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
The "user illusion" of this book's title comes from computer design and refers to thesimplistic mental image most of us have of our PCs. Our consciousness, says the author, is our user illusion of ourselves.
This book makes the case that humans are designed for a much richer existence than processing a dribble of data from a computer screen, which actually constitutes a form of sensory deprivation. That there is actually far too little information in the so-called Information Age may be responsible for the malaise of modern society, that nagging feeling that there must be more to life. There is—but we have to get outside and live life with all our senses to experience it more fully. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
I propose to consider the question, “Can machines think?”♣ This should begin with definitions of the meaning of the terms “machine” and “think”. The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous. If the meaning of the words “machine” and “think” are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, “Can machines think?” is to be sought in a statistical survey such as a Gallup poll.
The Turing Test was proposed by Alan Turing in 1950; he called it the Imitation Game. In 1991 Hu Loebner prize competition, offering a f h Loebner started the 100,000 prize to the author of the first computer program to pass an unrestricted Turing test. Annual competitions are held each year with smaller prizes for the best program on a restricted Turing test. This paper describes the development of one such Turing System, including the technical design of the program and its performance on the first three Loebner Prize competitions. We also discuss the program's four year development effort, which has depended heavily on constant interaction with people on the Internet via Tinymuds (multiuser network communication servers). Finally, we discuss the design of the Loebner com- petition itself, and address its usefulness in furthering the development of Artificial Intelligence.
Eliza is a program operating within the MAC time-sharing system at MIT which makes certain kinds of natural language conversation between man and computer possible. Input sentences are analyzed on the basis of decomposition rules which are triggered by key words appearing in the input text. Responses are generated by reassembly rules associated with selected decomposition rules. The fundamental technical problems with which Eliza is concerned are: (1) the identification of key words, (2) the discovery of minimal context, (3) the choice of appropriate transformations, (4) generation of responses in the absence of key words, and (5) the provision of an editing capability for Eliza scripts. A discussion of some psychological issues relevant to the Eliza approach as well as of future developments concludes the paper. 9 references.
In ‘Computing Machinery and Intelligence’, Alan Turing actually proposed not one, but two, practical tests for deciding the question ‘Can a machine think?’ He presented them as equivalent. I show here that the first test described in that much-discussed paper is in fact not equivalent to the second one, which has since become known as ‘the Turing Test’. Although the first, neglected, test uses a human’s linguistic performance in setting an empirical test of intelligence, it does not make behavioral similarity to that performance the criterion of intelligence. The two tests yield different results, and the first provides a more appropriate measure of intelligence.
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Turing's Two Tests for Intelligence Computing Machinery and Intelligence ELIZA—A Computer Program for the Study of Naturaanguage Communication between Man and Machine
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[Weizenbaum 1976]
Excerpts from Report of Bill Clinton’s grand jury testimony in Washington Post
- W Clinton
The Emporer’s New Mind
- R Penrose
Stoned Machines and Very Human Humans: The Politics of Passing and Outing in the Loebner Contest
- S Zdenek
- S. Zdenek
RACTER, posted to the comp.ai.* hierarchy
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The Turk, Chess Automaton
- G M Levitt
- G.M. Levitt