Agent-based agreement over concept meaning using contrast sets

We develop a model for 2 agents to reach agreement over concept meaning in specific contexts. The model is based on an argumentation-based communication that engage the agents in a process of mutual adaptation using argumentation to reach an agreement over concept meaning. Our approach is to model concept meaning using the semiotic triangle and the notion of contrast sets. We implement and evaluate present three common sense scenarios where two agents argue and reach agreements over the contextual meaning of concepts. © 2015 The authors and IOS Press. All rights reservedThis paper has been partially supported by projects ESSENCE: Evolution of Shared Semantics in Computational Environments (TIN 607062) and NASAID (CSIC Intramural 201550E022)Peer Reviewe

Coordinated inductive learning using argumentation-based communication

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-MAIL, an argumentation approach for agents to argue about hypotheses learnt by induction. A-MAIL integrates, in a single framework, the capabilities of learning from experience, communication, hypothesis revision and argumentation. Therefore, the A-MAIL approach is one step further in achieving autonomous agents with learning capabilities which can use, communicate and reason about the knowledge they learn from examples. © 2014, The Author(s).Research partially funded by the projects Next-CBR (TIN2009-13692-C03-01) and Cognitio (TIN2012-38450- C03-03) [both co-funded with FEDER], Agreement Technologies (CONSOLIDER CSD2007-0022), and by the Grants 2009-SGR-1433 and 2009-SGR-1434 of the Generalitat de Catalunya.Peer reviewe

Similarity measures over refinement graphs

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 define and analyze two similarity measures, S λ and S π, based on refinement graphs. The anti-unification-based similarity, S λ, assesses similarity by finding the anti-unification of two instances, which is a description capturing all the information common to these two instances. The property-based similarity, S π, is based on a process of disintegrating the instances into a set of properties, and then analyzing these property sets. Moreover these similarity measures are applicable to any representation language for which a refinement graph that satisfies the requirements we identify can be defined. Specifically, we present a refinement graph for feature terms, in which several languages of increasing expressiveness can be defined. The similarity measures are empirically evaluated on relational data sets belonging to languages of different expressiveness. © 2011 The Author(s).Support for this work came from the project Next-CBR TIN2009-13692-C03-01 (co-sponsored by EU FEDER funds)Peer Reviewe

Democracy Models and Civic Technologies: Tensions, Trilemmas, and Trade-offs

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 technologies in mitigating these tensions and trilemmas. Third, it proposes to adopt a meso-level approach, in between the macro-level of democratic theories and the micro-level of tools, to situate the interplay between people, digital technologies, and data.Comment: Workshop on 'Linked Democracy: AI for Democratic Innovation' (IJCAI2017), Melbourne, August 19, 201

Symbolic Explanation of Similarities in Case-based Reasoning

CBR systems solve problems by assessing their similarity with already solved problems (cases). Explanation of a CBR system prediction usually consists of showing the user the set of cases that are most similar to the current problem. Examining those retrieved cases the user can then assess whether the prediction is sensible. Using the notion of symbolic similarity, our proposal is to show the user a symbolic description that makes explicit what the new problem has in common with the retrieved cases. Specifically, we use the notion of anti-unification (least general generalization) to build symbolic similarity descriptions. We present an explanation scheme using anti-unification for CBR systems applied to classification tasks. This scheme focuses on symbolically describing what is shared between the current problem and the retrieved cases that belong to different classes. Examining these descriptions of symbolic similarities the user can assess which aspects are determining that a problem is classified one way or another. The paper exemplifies this proposal with an implemented application of the symbolic similarity scheme to the domain of predicting the carcinogenic activity of chemical compounds

El medi social i cultural en Educació Infantil

Departament d'Educació. Codi d’assignatura: MI101

Analogy, Amalgams, and Concept Blending

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 creativity. In this paper, we provide an intermediate report on work towards the development of a computational-level and algorithmic-level account of concept blending. We present the theoretical background as well as an algorithmic proposal combining techniques from computational analogy-making and case-based reasoning, and exemplify the feasibility of the approach in two case studies.. © 2015 Cognitive Systems Foundation.The authors acknowledge the financial support of the Future and Emerging Technologies programme within the Seventh Framework Programme for Research of the European Commission, under FET-Open grant number: 611553 (COINVENT)Peer Reviewe

Sentiment and preference guided social recommendation

© Springer International Publishing Switzerland 2014. 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 a preference ranking. We demonstrate the utility of our proposed formalism by conducting a comparative analysis on data extracted from Amazon.com. In particular we show that the performance of the proposed aspect based sentiment analysis algorithm is superior to existing aspect extraction algorithms and that combining this with preference knowledge leads to better recommendations.This research has been partially supported by AGAUR Scholarship (2013FI-B 00034) and Project Cognitio TIN2012-38450-C03-03Peer Reviewe

Special issue on logics and artificial intelligence

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 relation between logics and AI and collects recent research works on logic-based approaches in AI

Upward Refinement for Conceptual Blending in Description Logic — An ASP-based Approach and Case Study in EL++—

Conceptual blending is understood to be a process that serves a variety of cognitive purposes, including creativity, and has been highly influential in cognitive linguistics. In this line of thinking, human creativity is modeled as a blending process that takes different mental spaces as input and combines them into a new mental space, called a blend. According to this form of combinatorial creativity, a blend is constructed by taking the existing commonalities among the input mental spaces—known as the generic space—into account, and by projecting their structure in a selective way. Since input spaces for interesting blends are often initially incompatible, a generalisation step is needed before they can be blended. In this paper, we apply this idea to blend input spaces specified in the description logic EL++ and propose an upward refinement operator for generalising EL++ concepts. We show how the generalisation operator is translated to Answer Set Programming (ASP) in order to implement a search process that finds possible generalisations of input concepts. We exemplify our approach in the domain of computer icons.COINVENT European Commission FP7 - 611553Peer reviewe