Nick Bassiliades

Verified

Nick verified their affiliation via an institutional email.

Learn more

Verified

Nick verified their affiliation via an institutional email.

Learn more

• PhD Informatics
• Professor (Full) at Aristotle University of Thessaloniki

Traditional analytical frameworks often struggle to capture the complexity of business ecosystems, leading to ecosystem blindspots and missed opportunities. Following a semantic approach, we introduce the Business Ecosystem Analysis & Representation (BEAR) framework to uncover these blindspots. This approach leverages domain, seed ontologies, and e...

The current study focuses on the development of an open-source framework which is outsourcing the lack of expressivity of the standardized Planning Domain Definition Language – PDDL, leveraging the capacity and flexibility of the hosting environment in Python 3.8. The implementation was based on the definition and logic of a PDDL domain modeling an...

In the previous two decades, Knowledge Graphs (KGs) have evolved, inspiring developers to build ever-more context-related KGs. Because of this development, Artificial Intelligence (AI) applications can now access open domain-specific information in a format that is both semantically rich and machine comprehensible. In this article, we introduce the...

To implement Open Governance a crucial element is the efficient use of the big amounts of open data produced in the public domain. Public administration is a rich source of data and potentially new knowledge. It is a data intensive sector producing vast amounts of information encoded in government decisions and acts, published nowadays on the World...

The most popular technique for specification requirements is natural language. The disadvantage of natural language is ambiguity. Boilerplates are syntactic patterns which limit the ambiguity problem associated with using natural language to specify system/software requirements. Also, using boilerplates is considered a useful tool for inexperienced...

Automated Machine Learning-based systems’ integration into a wide range of tasks has expanded as a result of their performance and speed. Although there are numerous advantages to employing ML-based systems, if they are not interpretable, they should not be used in critical or high-risk applications. To address this issue, researchers and businesse...

Documents contain textual information, which is of the utmost importance for all the organizations. Document management systems have been used to store vast amounts of unstructured textual data described with minimal metadata, a method that has several limitations. In order to convert hidden knowledge into machine-readable data with rich connection...

Without doubt Prolog, as the most prominent member of the logic programming (LP) approach, presents significant differences from the mainstream programming paradigms. However, demonstrating its flexibility to larger audiences can indeed be a challenging task, since the declarative style of LP lies outside the mainstream programming languages most a...

Nowadays, a large number of cargo vessels arrive at ports and need to be serviced on a daily basis. This creates a hard to solve problem related to finding the most efficient order to service these vessels. In this context, we study the problem of scheduling the service of cargo vessels arriving at a single port and considering certain spatial and...

Trauma patients are commonly severely injured people that require systematic evaluation and rapid response. This paper presents work in progress for an explainable, late fusion and Deep Learning-based prediction system for interventions in Intensive Care Units (ICU) by employing neurosumbolic Explainable Artificial Intelligence (XAI) techniques. Th...

Multi-label classification is a challenging task, particularly in domains where the number of labels to be predicted is large. Deep neural networks are often effective at multi-label classification of images and textual data. When dealing with tabular data, however, conventional machine learning algorithms, such as tree ensembles, appear to outperf...

argumentation frameworks (AAFs), introduced by Dung (1995, Artif. Intell., 228, 321–357), enabled a new way of reasoning with arguments, which does not take into account the internal structure of arguments but only how they are related to each other. The only form of relation considered in AAFs is a binary attack relation on the set of arguments. F...

Automated Machine Learning-based systems' integration into a wide range of tasks has expanded as a result of their performance and speed. Although there are numerous advantages to employing ML-based systems, if they are not interpretable, they should not be used in critical, high-risk applications where human lives are at risk. To address this issu...

We present our current state of research on explaining non-entailments by finding isomorphic subtrees of EL-description trees. Our approach extends the set of abducible axioms that consist only of concepts to role restrictions as well. We argue how our approach could find solutions to abduction problems in scenarios where other methods cannot, and...

During a Mass Casualty Incident, it is essential to make effective decisions to save lives and nursing the injured. This paper presents a work in progress on the design and development of an explainable decision support system, intended for the medical personnel and care givers, that capitalises on multiple modalities to achieve situational awarene...

System requirements specify how a system meets stakeholder needs. They are a partial definition of the system under design in natural language that may be restricted in syntax terms. Any natural language specification inevitably lacks a unique interpretation and includes underspecified terms and inconsistencies. If the requirements are not validate...

Multi-label classification is a challenging task, particularly in domains where the number of labels to be predicted is large. Deep neural networks are often effective at multi-label classification of images and textual data. When dealing with tabular data, however, conventional machine learning algorithms, such as tree ensembles, appear to outperf...

In critical situations involving discrimination, gender inequality, economic damage, and even the possibility of casualties, machine learning models must be able to provide clear interpretations of their decisions. Otherwise, their obscure decision-making processes can lead to socioethical issues as they interfere with people’s lives. Random forest...

Artificial Intelligence (AI) is having an enormous impact on the rise of technology in every sector. Indeed, AI-powered systems are monitoring and deciding on sensitive economic and societal issues. The future is moving towards automation, and we must not prevent it. Many people, though, have opposing views because of the fear of uncontrollable AI...

Dimensionality reduction (DR) is a popular method for preparing and analyzing high-dimensional data. Reduced data representations are less computationally intensive and easier to manage and visualize, while retaining a significant percentage of their original information. Aside from these advantages, these reduced representations can be difficult o...

The industry related to electric vehicles (EVs) has seen a substantial increase in recent years, as such vehicles have the ability to significantly reduce total CO2 emissions and the related global warming effect. In this paper, we focus on the problem of allocating EVs to charging stations, scheduling and pricing their charging. Specifically, we d...

This work describes the architecture of the back-end engine of a real-time traffic data processing and satellite navigation system. The role of the engine is to process real-time feedback, such as speed and travel time, provided by in-vehicle devices and derive real-time reports and traffic predictions through leveraging historical data as well. We...

Recently, many question answering systems that derive answers from linked data repositories have been developed. The purpose of this survey is to identify the common features and approaches of the semantic question answering (SQA) systems, although many different and prototype systems have been designed. The SQA systems use a formal query language...

Document Management Systems (DMS) are used for decades to store large amounts of information in textual form. Their technology paradigm is based on storing vast quantities of textual information enriched with metadata to support searchability. However, this exhibits limitations as it treats textual information as black box and is based exclusively...

The authors present a knowledge retrieval framework for the household domain enhanced with external knowledge sources that can argue over the information that it returns and learn new knowledge through an argumentation dialogue. The framework provides access to commonsense knowledge about household environments and performs semantic matching betwee...

Development of control algorithms for enhancing performance in safety-critical systems such as the Autonomous Emergency Braking (AEB) system is an important issue in the emerging field of automated electric vehicles. In this study, we model a safety distance-based hierarchical AEB control system constituted of a high-level Rule-Based Supervisory co...

This paper presents a novel abstract argumentation framework, called Multi-Attack Argumentation Framework (MAAF), which supports different types of attacks. The introduction of types gives rise to a new family of non-standard semantics which can support applications that classical approaches cannot, while also allowing classical semantics as a spec...

The processes involved in requirements engineering are some of the most, if not the most, important steps in systems development. The need for well-defined requirements remains a critical issue for the development of any system. Describing the structure and behavior of a system could be proven vague, leading to uncertainties, restrictions, or impro...

Infusing autonomous artificial systems with knowledge about the physical world they inhabit is of utmost importance and a long-lasting goal in Artificial Intelligence (AI) research. Training systems with relevant data is a common approach; yet, it is not always feasible to find the data needed, especially since a big portion of this knowledge is co...

Argumentative discourse rarely consists of opinions whose claims apply universally. As with logical statements, an argument applies to specific objects in the universe or relations among them, and may have exceptions. In this paper, we propose an argumentation formalism that allows associating arguments with a domain of application. Appropriate sem...

The use of machine learning rapidly increases in high-risk scenarios where decisions are required, for example in healthcare or industrial monitoring equipment. In crucial situations, a model that can offer meaningful explanations of its decision-making is essential. In industrial facilities, the equipment's well-timed maintenance is vital to ensur...

Development of control algorithms for enhancing performance in safety-critical systems such as the Autonomous Emergency Braking system (AEB) is an important issue in the emerging field of automated electric vehicles. In this study, we design a safety distance-based hierarchical AEB control system constituted of a high-level Rule-Based Supervisory c...

Artificial Intelligence (AI) has a tremendous impact on the unexpected growth of technology in almost every aspect. AI-powered systems are monitoring and deciding about sensitive economic and societal issues. The future is towards automation, and it must not be prevented. However, this is a conflicting viewpoint for a lot of people, due to the fear...

In critical situations involving discrimination, gender inequality, economic damage, and even the possibility of casualties, machine learning models must be able to provide clear interpretations for their decisions. Otherwise, their obscure decision-making processes can lead to socioethical issues as they interfere with people's lives. In the afore...

Argumentation and eXplainable Artificial Intelligence (XAI) are closely related, as in the recent years, Argumentation has been used for providing Explainability to AI. Argumentation can show step by step how an AI System reaches a decision; it can provide reasoning over uncertainty and can find solutions when conflicting information is faced. In t...

The use of machine learning rapidly increases in high-risk scenarios where decisions are required, for example in healthcare or industrial monitoring equipment. In crucial situations, a model that can offer meaningful explanations of its decision-making is essential. In industrial facilities, the equipment's well-timed maintenance is vital to ensur...

Artificial Intelligence (in Greek). Η ύλη καλύπτει εκτενώς τόσο τη συμβολική όσο και την υπολογιστική τεχνητή νοημοσύνη και δεν παραλείπει να συμπεριλάβει και νέα θέματα μεγάλου ερευνητικού και εμπορικού ενδιαφέροντος. Αρκετά από αυτά καλύπτονται εκτενώς, κάνοντας το βιβλίο κατάλληλο για χρήση τόσο σε προπτυχιακά όσο και σε μεταπτυχιακά μαθήματα.

Interpretable machine learning is an emerging field providing solutions on acquiring insights into machine learning models' rationale. It has been put in the map of machine learning by suggesting ways to tackle key ethical and societal issues. However, existing techniques of interpretable machine learning are far from being comprehensible and expla...

In the field of domestic cognitive robotics, it is important to have a rich representation of knowledge about how household objects are related to each other and with respect to human actions. In this paper, we present a domain dependent knowledge retrieval framework for household environments which was constructed by extracting knowledge from the...

The field of Computational Argumentation is well-tailored to approach commonsense reasoning, due to its ability to model contradictory information. In this paper, we present preliminary work on how an argumentation framework can explicitly model commonsense knowledge, both at a logically structured and at an abstract level. We discuss the correlati...

We study the problem of allocating Electric Vehicles (EVs) to charging stations and scheduling their charging. We develop offline and online solutions that treat EV users as self-interested agents that aim to maximise their profit and minimise the impact on their schedule. We formulate the problem of the optimal EV to charging station allocation as...

This work describes the architecture of the back-end engine of a real-time traffic data processing and satellite navigation system. The role of the engine is to process real-time feedback, such as speed and travel time, provided by in-vehicle devices and derive real-time reports and traffic predictions through leveraging historical data as well. We...

Technological breakthroughs on smart homes, self-driving cars, health care and robotic assistants, in addition to reinforced law regulations, have critically influenced academic research on explainable machine learning. A sufficient number of researchers have implemented ways to explain indifferently any black box model for classification tasks. A...

Semantic web rule language (SWRL) combines web ontology language (OWL) ontologies with horn logic rules of the rule markup language (RuleML) family. Being supported by ontology editors, rule engines and ontology reasoners, it has become a very popular choice for developing rule-based applications on top of ontologies. However, SWRL is probably not...

This book constitutes the revised post-conference proceedings of the 17th European Conference on Multi-Agent Systems, EUMAS 2020, and the 7th International Conference on Agreement Technologies, AT 2020, which were originally planned to be held as a joint event in Thessaloniki, Greece, in April 2020. Due to COVID-19 pandemic the conference was postp...

Object perception is a fundamental sub-field of Computer Vision, covering a multitude of individual areas and having contributed high-impact results. While Machine Learning has been traditionally applied to address related problems, recent works also seek ways to integrate knowledge engineering in order to expand the level of intelligence of the vi...

We consider the problem of scheduling Electric Vehicle (EV) charging within a set of multiple charging stations. Each station aims to maximize the amount of charged energy and the number of charged EVs. We propose an agent-based simulation scheme, where the EVs announce their requests to the stations and each station computes an optimal solution us...

Towards a future where machine learning systems will integrate into every aspect of people's lives, researching methods to interpret such systems is necessary, instead of focusing exclusively on enhancing their performance. Enriching the trust between these systems and people will accelerate this integration process. Many medical and retail banking...

Over the last years, the Internet of Things attracted much attention mainly due to its potential to change our daily life. It attempts to create a world where everyone and everything will be connected while knowledge will be diffused effortlessly. Yet, this open, distributed and heterogeneous environment raises important challenges, such as intelli...

Technological breakthroughs on smart homes, self-driving cars, health care and robotic assistants, in addition to reinforced law regulations, have critically influenced academic research on explainable machine learning. A sufficient number of researchers have implemented ways to explain indifferently any black box model for classification tasks. A...

We propose offline and online scheduling algorithms for the charging of electric vehicles (EVs) in a single charging station (CS). The station has available cheaper, but limited, energy from renewable energy sources (RES). The EVs are capable of and willing to participate in vehicle-to-vehicle (V2V) energy transfers that are used to reduce the char...

Nowadays university rankings are ubiquitous commodities; a plethora of them is published every year by private enterprises, state authorities and universities. University rankings are very popular to governments, journalists, university administrations and families as well. At the same time, they are heavily criticized as being very subjective and...

We study a setting where electric vehicles (EVs) can be hired to drive from pick-up to drop-off stations in a mobility-on-demand (MoD) scheme. Each point in the MoD scheme is equipped with battery charge facility to cope with the EVs’ limited range. Customer-agents announce their trip requests over time, and the goal for the system is to maximize t...

p>Electric Vehicles (EVs) are considered an efficient alternative to internal combustion engined ones, aiming to reduce global CO₂ emissions. In the last years, EVs are entering the market in an increasing pace. In contrast to conventional cars, EVs have a more complicated recharging procedure. Thus, the development of tools for the effi...

The impact of the Internet of Things will transform business and economy. This network of intercommunicating heterogeneous Things is expected to affect the commerce industry by driving innovation and new opportunities in the future. Yet, this open, distributed and heterogeneous environment raises challenges. Old eCommerce practices cannot be suffic...

In this paper, we develop a novel computational argumentation framework for resolving conflicts that arise in a community of multiple stakeholders where each one of them bears a private policy/strategy for shared and inter-related decisions. Decisions taken individually by stakeholders can be contradicting, so there is a need for an arbitration ser...

The Internet of Things is a network of objects, called Things, which can interact with the environment or other Things, with no human intervention. At the same time, multi-agent systems are considered a modern medium of communication and interaction with limited or no human intervention. Hence, combining agent technology with the Internet of Things...

Electric Vehicles (EVs) are considered an efficient alternative to internal combustion engined ones, aiming to reduce global CO 2 emissions. In the last years, EVs are entering the market in an increasing pace. In contrast to conventional cars, EVs have a more complicated recharging procedure. Thus, the development of tools for the efficient simula...

Successful Location-Based Services should offer accurate and timely information consumption recommendations to their customers, relevant to their contextual situation. To achieve this and provide the best available recommendations to the user, researchers and developers analyse available data via exploiting data mining techniques. Unfortunately, in...

We study a setting where Electric Vehicles (EVs) can be hired to drive from pick-up to drop-off points in a Mobility-on-Demand (MoD) scheme. The goal of the system is, either to maximize the number of customers that are serviced, or the total EV utilization. To do so, we characterise the optimisation problem as a max-flow problem in order to determ...

Electric Vehicles (EVs) are considered an efficient alternative to internal combustion engined ones, aiming to reduce global CO2 emissions. In the last years, EVs are entering the market in an increasing pace. In contrast to conventional cars, EVs have a more complicated recharging procedure. Thus, the development of tools for the efficient simulat...

This book contains extended versions of the best papers presented at the 13th International Conference on Information and Communication Technologies in Education, Research, and Industrial Applications, ICTERI 2017, held in Kyiv, Ukraine, in May 2017. The 11 revised full papers included in this volume were carefully reviewed and selected from 151 in...

SWRL is a semantic web rule language that combines OWL ontologies with Horn Logic rules of the RuleML family of rule languages, extending the set of OWL axioms to include Horn-like rules. Being supported by the Prot\'eg\'e ontology editor as well as by popular rule engines and ontology reasoners, such as Jess, Drools and Pellet, SWRL has become a v...

In recent years, there is a rush in Artificial Intelligence (AI) research to produce practical solutions for the Smart Grid, the anticipated new generation of energy (primarily electricity) networks that will be able to make efficient use of renewable energy sources, support real time and efficient demand response, as well as the large-scale deploy...

PaaS is a Cloud computing service that provides a computing platform to develop, run, and manage applications without the complexity of infrastructure maintenance. SMEs are reluctant to enter the growing PaaS market due to the possibility of being locked in to a certain platform, mostly provided by the market's giants. The PaaSport Marketplace aims...