Enric Plaza

Enric Plaza
Spanish National Research Council | CSIC · Artificial Intelligence Research Institute

Ph.D

About

251
Publications
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9,045
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January 2001 - December 2007
Spanish National Research Council

Publications

Publications (251)
Chapter
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The literature on diagrammatic reasoning includes theoretical and experimental work on the effectiveness of diagrams for conveying information. One influential theoretical contribution to this field proposes that a notation that is more effective than another would have an observational advantage over it; that is, it would make certain pieces of in...
Chapter
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In this paper, we present a model of the sense-making process for diagrams, and describe it for the case of Hasse diagrams. Sense-making is modeled as the construction of networks of conceptual blends among image schemas and the diagram’s geometric configuration. As a case study, we specify four image schemas and the geometric configuration of a Ha...
Chapter
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In this work, we propose a formal, computational model of the sense-making of diagrams by using the theories of image schemas and conceptual blending, stemming from cognitive linguistics. We illustrate our model here for the case of a Hasse diagram, using typed first-order logic to formalise the image schemas and to represent the geometry of a diag...
Article
We present a mathematical model for the cognitive operation of conceptual blending that aims at being uniform across different representation formalisms, while capturing the relevant structure of this operation. The model takes its inspiration from amalgams as applied in case-based reasoning, but lifts them into category theory so as to follow Jose...
Article
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Computational models of novel concept understanding and creativity are addressed in this paper from the viewpoint of conceptual blending theory (CBT). In our approach, a novel, unknown concept is addressed in a communication setting, where this novel concept, created as a blend by an emitter agent, sends a communicative object (words, or in this pa...
Article
This special issue proposes a space for researchers to discuss the challenges of bridging pattern recognition and cognitive assistants. This bridge was travelled in both sides. From one side, how pattern recognition methods can help intelligent systems to become more cognitive. And from the other side, how cognitive models can improve pattern recog...
Article
There is a significant range of ongoing challenges in artificial intelligence (AI) dealing with reasoning, planning, learning, perception and cognition, among others. In this scenario, many-valued logics emerge as one of the topics in many of the solutions to some of those AI problems. This special issue presents a brief introduction to the relatio...
Chapter
In this chapter, we present a computational framework that models concept invention. The framework is based on and extends conceptual blending. Apart from the blending mechanism modeling the creation of new concepts, the framework considers two extra dimensions, namely, origin and destination. For the former, we describe how a Rich Background suppo...
Chapter
This chapter is a theoretical exploration of Joseph Goguen’s category-theoretic model of conceptual blending and presents an alternative proposal to model blending as amalgams, which were originally proposed as a method for knowledge transfer in case-based reasoning. The chapter concludes with a generalisation of the amalgam-based model by relating...
Article
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We present a computational framework for conceptual blending, a concept invention method that is advocated in cognitive science as a fundamental and uniquely human engine for creative thinking. Our framework treats a crucial part of the blending process, namely the generalisation of input concepts, as a search problem that is solved by means of mod...
Article
Concept blending – a cognitive process which allows for the combination of certain elements (and their relations) from originally distinct conceptual spaces into a new unified space combining these previously separate elements, and enables reasoning and inference over the combination – is taken as a key element of creative thought and combinatorial...
Conference Paper
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The Workshop on ‘Linked Democracy: Artificial Intelligence for Democratic Innovation’ is one of the official workshops of the International Joint Conference on Artificial Intelligence (IJCAI 2017) held in Melbourne (19-26 August 2017). The goal of this workshop is to provide a multidisciplinary forum to address questions such as: How to model the i...
Article
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In this paper we focus on a particular interesting type of web user-generated content: people's experiences. We extend our previous work on aspect extraction and sentiment analysis and propose a novel approach to creating a vocabulary of basic level concepts with the appropriate granularity to characterize a set of products. This concept vocabulary...
Article
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This paper aims at connecting democratic theory with civic technologies in order to highlight the links between some theoretical tensions and trilemmas and design trade-offs. First, it reviews some tensions and trilemmas raised by political philosophers and democratic theorists. Second, it considers both the role and the limitations of civic techno...
Conference Paper
Full-text available
In this paper we focus on a particular interesting web user-generated content: people’s experiences. We extend our previous work on aspect extraction and sentiment analysis and propose a novel approach to create a vocabulary of basic level concepts with the appropriate granularity to characterize a set of products. This concept vocabulary is create...
Article
Conceptual blending is a mental process that serves a variety of cognitive purposes, including human creativity. In this line of thinking, human creativity is modeled as a process that takes different mental spaces as input and combines them into a new mental space, called a blend. According to this form of combinational creativity, a blend is cons...
Conference Paper
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This paper presents a new approach to case revision in case-based reasoning based on the idea of argumentation. Previous work on case reuse has proposed the use of operations such as case amalgamation (or merging), which generate solutions by combining information coming from different cases. Such approaches are often based on exploring the search...
Conference Paper
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In this paper, we extend our previous work on social recommender systems to harness knowledge from product reviews. By mining product reviews, we can exploit sentiment-rich content to ascertain user opinion expressed over product aspects. Aspect aware sentiment analysis provides a more structured approach to product comparison. However, aspects ext...
Conference Paper
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We present a framework for conceptual blending – a concept invention method that is advocated in cognitive science as a fundamental, and uniquely human engine for creative thinking. Herein, we employ the search capabilities of ASP to find commonalities among input concepts as part of the blending process , and we show how our approach fits within a...
Article
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Similarity assessment is a key operation in several areas of artificial intelligence. This paper focuses on measuring similarity in the context of Description Logics (DL), and specifically on similarity between individuals. The main contribution of this paper is a novel approach based on measuring similarity in the space of Conjunctive Queries, rat...
Article
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In cooperation with the Association for the Advancement of Artificial Intelligence (AAAI), the Twenty-Second International Conference on Case-Based Reasoning (ICCBR), the premier international meeting on research and applications in case-based reasoning (CBR), was held from Monday September 29 to Wednesday October 1, 2014, in Cork, Ireland. ICCBR i...
Article
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The Artificial Intelligence Research Institute (IIIA) is a public research centre, belonging to the Spanish National Research Council (CSIC), dedicated to AI research. We focus our activities on a few well-defined sub-domains of Artificial Intelligence, positively avoiding dispersion and keeping a good balance between basic research and application...
Article
We present a new approach lo learn from relational data based on re-representation of the examples. This approach, called property-based re-representation is based on a new analysis of the structure of refinement graphs used in ILP and relational learning in general. This analysis allows the characterization of relational examples by a set of multi...
Chapter
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Capturing users’ preference that change over time is a great challenge in recommendation systems. What makes a product feature interesting now may become the accepted standard in the future. Social recommender systems that harness knowledge from user expertise and interactions to provide recommendation have great potential in capturing such trendin...
Conference Paper
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Social recommender systems harness knowledge from social experiences, expertise and interactions. In this paper we focus on two such knowledge sources: sentiment-rich user generated reviews; and preferences from purchase summary statistics. We formalise the integration of these knowledge sources by mixing a novel aspect-based sentiment ranking with...
Article
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This paper focuses on coordinated inductive learning, concerning how agents with inductive learning capabilities can coordinate their learnt hypotheses with other agents. Coordination in this context means that the hypothesis learnt by one agent is consistent with the data known to the other agents. In order to address this problem, we present A-MA...
Conference Paper
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The SINTELNET WG5 Workshop on Crowd Intelligence was held in Barcelona on 8-9 January 2014. The European Network for Social Intelligence (SINTELNET) regularly organizes Interdisciplinary Workshops to explore and discuss the interplay of humanities, philosophy, social science and information technologies around key social intelligence notions. T...
Book
This book constitutes the refereed proceedings of the 21st International Conference on Case-Based Reasoning Research and Development (ICCBR 2014) held in Cork, Ireland, in September 2014. The 35 revised full papers presented were carefully reviewed and selected from 49 submissions. The presentations cover a wide range of CBR topics of interest both...
Conference Paper
Full-text available
Similarity assessment is a key operation in case-based reasoning and other areas of artificial intelligence. This paper focuses on measuring similarity in the context of Description Logics (DL), and specifically on similarity between individuals. The main contribution of this paper is a novel approach based on measuring similarity in the space of C...
Article
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In this paper we introduce aa interagent as an au- tonomous software agent which manages (intermedi- ates) the communication and coordination between an agent and the agent society wherein this is situated. With this aim, we have developed JIM, a general- purpose interagent that provides agents with a highly versatile range of programmable --before...
Conference Paper
We present a new approach lo learn from relational data based on re-representation of the examples. This approach, called property-based re-representation is based on a new analysis of the structure of refinement graphs used in ILP and relational learning in general. This analysis allows the characterization of relational examples by a set of multi...
Article
This paper introduces a logical model of inductive generalization, and specifically of the machine learning task of inductive concept learning (ICL). We argue that some inductive processes, like ICL, can be seen as a form of defeasible reasoning. We define a consequence relation characterizing which hypotheses can be induced from given sets of exam...
Conference Paper
We present a general framework for addressing the problem of semantic intelligibility among artificial agents based on concepts integral to the case-based reasoning research program. For this purpose, we define case-based semiotics (CBS) (based on the well known notion of the semiotic triangle) as the model that defines semantic intelligibility. We...
Conference Paper
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This paper presents a new approach for solving the Resource-Constrained Project Scheduling Problem using Case-Based Reasoning in a constructive way. Given a project to be scheduled our method retrieves similar projects scheduled in the past, selects the most similar project, and reuses as much as possible from the old solution to build a schedule f...
Conference Paper
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While similarity and retrieval in case-based reasoning (CBR) have received a lot of attention in the literature, other aspects of CBR, such as case reuse are less understood. Specifically, we focus on one of such, less understood, problems: knowledge transfer. The issue we intend to elucidate can be expressed as follows: what knowledge present in a...
Article
Full-text available
Similarity also plays a crucial role in support vector machines. Similarity assessment plays a key role in lazy learning methods such as k-nearest neighbor or case-based reasoning. In this paper we will show how refinement graphs, that were originally introduced for inductive learning, can be employed to assess and reason about similarity. We will...
Conference Paper
Full-text available
While similarity and retrieval in case-based reasoning (CBR) have received a lot of attention in the literature, other aspects of CBR, such as case reuse are less understood. Specifically, we focus on one of such, less understood, problems: knowledge transfer. The issue we intend to elucidate can be expressed as follows: what knowledge present in a...
Conference Paper
Full-text available
In this paper we propose an Open Access model for Legal Information Institutes (LIIs) publications in three steps: Accredited Public Archival (APA), Comment-Open Publication (COP) and Peer-Reviewed Publication (PRP). This raises some ethical and legal issues on privacy and intellectual property which cannot be ignored. We would like to foster dialo...
Conference Paper
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Different agents in a multiagent system might have different solution quality or preference criteria. Therefore, when solving problems collaboratively using CBR, case reuse must take this into account. In this paper we propose ABARC, a model for multiagent case reuse, which divides case reuse in two stages: individual reuse, where agents generate f...
Conference Paper
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In this paper we focus on how to use CBR for making collective decisions in groups of agents. Moreover, we show that using CBR allows us to dispense with standard but unrealistic assumptions taken in these kind of tasks. Typically, social choice studies voting methods but assumes complete knowledge over all possible alternatives. We present a more...
Conference Paper
Full-text available
Similarity assessment is a key operation in many artificial intelligence fields, such as case-based reasoning, instance-based learning, ontology matching, clustering, etc. This paper presents a novel measure for assessing similarity between individuals represented using Description Logic (DL). We will show how the ideas of refinement operators and...
Article
Full-text available
Case-Based Reasoning CBR can give agents the capability of learning from their own experience and solve new problems, however, in a multi-agent system, the ability of agents to collaborate is also crucial. In this paper we present an argumentation framework AMAL designed to provide learning agents with collaborative problem solving joint deliberati...
Conference Paper
Full-text available
How to achieve shared meaning is a significant issue when more than one intelligent agent is involved in the same domain. We define the task of concept convergence, by which intelligent agents can achieve a shared, agreed-upon meaning of a concept (restricted to empirical domains). For this purpose we present a framework that, integrating computati...
Conference Paper
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Multiagent Inductive Learning is the problem that groups of agents face when they want to perform inductive learning, but the data of interest is distributed among them. This paper focuses on concept learning, and presents A-MAIL, a framework for multiagent induction integrating ideas from inductive learning, case-based reasoning and argumentation....
Conference Paper
Full-text available
How to reuse or adapt past solutions to new problems is one of the least understood problems in case-based reasoning. In this paper we will focus on the problem of how to combine solutions coming from multiple cases in search-based approaches to reuse. For that purpose, we introduce the notion of amalgam. Assuming the solution space can be characte...
Conference Paper
Full-text available
This paper presents an approach that integrates notions and techniques from two distinct fields of study —namely inductive learning and argumentation in multiagent systems (MAS). We will first discuss inductive learning and the role argumentation plays in multiagent inductive learning. Then we focus on how inductive learning can be used to realize...
Conference Paper
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The aim of this contribution is to demonstrate the feasibility of different processing techniques to identify phytoplankton assemblages when applied to oceanographic hyperspectral data sets (i.e. above surface measurements and vertical profiles). In order to address this issue and validate the proposed techniques, a simulated framework has been use...
Conference Paper
In this paper we propose an argumentation-based framework for multiagent induction, where two agents learn separately from individual training sets, and then engage in an argumentation process in order to converge to a common hypothesis about the data. The result is a multiagent induction strategy in which the agents minimize the set of cases that...
Conference Paper
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The work presented in this paper is part of a multidisciplinary team collaborating in the deployment of an autonomous oceanographic probe with the task of exploring marine regions and take phytoplankton samples for their subsequent analysis in a laboratory. We will describe an autonomous system that, from sensor data, is able to characterize phytop...
Conference Paper
Full-text available
Argumentation can be used by a group of agents to discuss about the validity of hypotheses. In this paper we propose an argumentation-based frame-work for multiagent induction, where two agents learn separately from individual training sets, and then engage in an argumentation process in order to converge to a common hypothesis about the data. The...
Conference Paper
Full-text available
This paper focuses on a logical model of induction, and specifically of the common machine learning task of inductive concept learning (ICL). We define an inductive derivation relation, which characterizes which hypothesis can be induced from sets of examples, and show its properties. Moreover, we will also consider the problem of communicating ind...
Conference Paper
Full-text available
Retrieval of structured cases using similarity has been studied in CBR but there has been less activity on defining similarity on description logics (DL). In this paper we present an approach that allows us to present two similarity measures for feature logics, a subfamily of DLs, based on the concept of refinement lattice. The first one is based o...
Article
Full-text available
The Web is a vibrant environment for innovation in computer science, AI, and social interaction; these innovations come in such great number and speed that it is almost impossible to follow them. This paper will focus on some emerging aspects on the web that are a great opportunity and challenge for AI, specifically the large amount of records of e...
Article
Full-text available
Argumentation can be used by a group of agents to discuss about the validity of hypotheses. In this paper we propose an argumentation- based framework for distributed induction, where two agents learn sep- arately from individual training sets, and then engage in an argumen- tation process in order to converge to a common hypothesis about the data....
Conference Paper
Full-text available
Case-Based Reasoning (CBR) can give agents the capability of learning from their own experience and solve new problems, however, in a multi-agent system, the ability of agents to collaborate is also crucial. In this paper we present an argumentation framework (AMAL) designed to provide learning agents with collaborative problem solving (joint delib...
Conference Paper
Full-text available
The Web is a vibrant environment for innovation in computer science, AI, and social interaction; these innovations come in such great number and speed that it is unlikely to follow them. This paper will focus on some emerging aspects on the web that are an opportunity and challenge for Case-based Reasoning, specifically the large amount of experien...
Conference Paper
Full-text available
“Similar problems have similar solutions” is a basic tenet of case-based inference. However this is not satisfied for CBR systems where the task is to achieve original solutions — i.e. solutions that, even for “old problems,” are required to be noticeably different from previously known solutions. This paper analyzes the role of reuse in CBR system...
Article
Full-text available
We present a dynamic electronic institutions approach for teamwork. In this model, agent teams are designed and deployed on-the- y so as to met the requirements of the task at hand. The result is a new form of electronic institution that is created dynamically out of existing components. We also introduce a case-based learning mechanism to form new...
Conference Paper
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In organisation schemes, musical artists are commonly iden- tified with a unique 'genre' label attached, even when they have affinity to multiple genres. To uncover this hidden cul- tural awareness about multi-genre affinity, we present a new model based on the analysis of the way in which a commu- nity of users organise artists and genres in playl...
Conference Paper
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The purpose of this paper is to investigate how argumentation pro- cesses among a group of agents may affect the outcome of group judgments. In particular we will focus on prediction markets (also called information mar- kets) and we will investigate how the existence of social networks (that allow agents to argue with one another to improve their...
Conference Paper
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Poolcasting is a social Web radio architecture in which groups of listeners influence in real time the music played on each channel. Poolcasting users contribute to the radio with songs they own, create radio channels and evaluate the proposed music, while an automatic intelligent technique schedules each channel with a group-customised sequence of...
Conference Paper
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This paper presents a knowledge-intensive Case-Based Rea- soning system to generate a sequence of songs customised for a commu- nity of listeners. To select each song in the sequence, rst a subset of songs musically associated with the last song of the sequence is retrieved from a music pool; then the preferences of the audience expressed as cases...
Article
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The concepts and methodology needed for design- ing, developing, and implementing real life applications based on multi-agent systems are today still a challenge for researchers in Artificial Intelligence and Computer Science. Industrial-strength multi-agent systems require, among other things, reusability, i.e. the capability of not having to desi...
Conference Paper
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This paper focuses of the group judgments obtained from a committee of agents that use deliberation. The deliberative process is realized by an argu- mentation framework called AMAL. The AMAL framework is completely based on learning from examples: the argument preference relation, the argument gen- eration policy, and the counterargument generatio...
Conference Paper
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ABSTRACT In this paper we will present an argumentation,framework,for learn- ing agents (AMAL) designed for two purposes: (1) for joint deliber- ation, and (2) for learning from communication. The AMAL frame- work is completely based on learning from examples: the argument preference relation, the argument generation policy, and the coun- terargume...
Conference Paper
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We present a proactive communication approach that allows CBR agents to gauge the strengths and weaknesses of other CBR agents. The communi- cation protocol allows CBR agents to learn from communicating with other CBR agents in such a way that each agent is able to retain certain cases provided by other agents that are able to improve their individ...
Conference Paper
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This paper addresses the issue of learning from com- munication among agents that work in the same do- main, are capable of learning from examples, and com- municate using an argumentative framework. We will present (1) an argumentation framework for Case-Based Reasoning agents and (2) an individual policy for agents to generate arguments and count...
Conference Paper
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We present a framework for teamwork based on a requirement’s driven dynamic composition approach to electronic institutions, which builds on an existing formalism for agent-mediated electronic institutions. In the presented framework, agent teams are designed and deployed on-the-fly so as to met the requirements of the problem at hand. The result i...
Article
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Community-driven services compile data provided by the community members, for instance playlists in Web 2.0 music sites. We show how this data can be analysed and knowledge about sequential as-sociations between songs and artists can be discovered. While most of this kind of analysis focus on (symmetric) similarity measures, we intend to discover w...