COINVENT: Towards a Computational Concept Invention Theory

We aim to develop a computationally feasible, cognitively-inspired, formal model of concept invention, drawing on Fauconnier and Turner’s theory of conceptual blending, and grounding it on a sound mathematical theory of concepts. Conceptual blending, although successfully applied to describing combinational creativity in a varied number of fields, has barely been used at all for implementing creative computational systems, mainly due to the lack of sufficiently precise mathematical characterisations thereof. The model we will define will be based on Goguen’s proposal of a Unified Concept Theory, and will draw from interdisciplinary research results from cognitive science, artificial intelligence, formal methods and computational creativity. To validate our model, we will implement a proof of concept of an autonomous computational creative system that will be evaluated in two testbed scenarios: mathematical reasoning and melodic harmonisation. We envisage that the results of this project will be significant for gaining a deeper scientific understanding of creativity, for fostering the synergy between understanding and enhancing human creativity, and for developing new technologies for autonomous creative systems.The project COINVENT acknowledges the nancial support of the Future and Emerging Tech- nologies (FET) programme within the Seventh Framework Programme for Research of the Eu- ropean Commission, under FET-Open Grant number: 611553Peer Reviewe

Replicator-interactor in experimental cultural knowledge evolution

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Ontology Alignment Evaluation in the Context of Multi-Agent Interactions

Abstract The most prominent way to assess the quality of an ontology alignment is to compute its precision and recall with respect to another alignment taken as reference. These measures determine, respectively, the proportion of found mappings that belong to the reference alignment and the proportion of the reference alignment that was found. The use of these values has been criticised arguing that they fail to reflect important semantic aspects. In addition, they rely on the existence of a reference alignment. In this work we discuss the evaluation of alignments when they are used to facilitate communication between heterogeneous agents. We introduce the notion of pragmatic alignment to refer to the mappings that let agents understand each other, and we propose new versions of precision and recall that measure how useful mappings are for a particular interaction. We then discuss practical applications of these new measures and how they can be estimated dynamically by interacting agents

Interaction-based ontology alignment repair with expansion and relaxation

euzenat2017aInternational audienceAgents may use ontology alignments to communicate when they represent knowledge with different ontologies: alignments help reclassifying objects from one ontology to the other. These alignments may not be perfectly correct, yet agents have to proceed. They can take advantage of their experience in order to evolve alignments: upon communication failure, they will adapt the alignments to avoid reproducing the same mistake. Such repair experiments had been performed in the framework of networks of ontologies related by alignments. They revealed that, by playing simple interaction games, agents can effectively repair random networks of ontologies. Here we repeat these experiments and, using new measures, show that previous results were underestimated. We introduce new adaptation operators that improve those previously considered. We also allow agents to go beyond the initial operators in two ways: they can generate new correspondences when they discard incorrect ones, and they can provide less precise answers. The combination of these modalities satisfy the following properties: (1) Agents still converge to a state in which no mistake occurs. (2) They achieve results far closer to the correct alignments than previously found. (3) They reach again 100% precision and coherent alignments

Framework for the semantic alignment of enterprise’s domain knowledge

Nowadays, the consumption of goods and services on the Internet are increasing in a constant motion. Small and Medium Enterprises (SMEs) mostly from the traditional industry sectors are usually make business in weak and fragile market sectors, where customized products and services prevail. To survive and compete in the actual markets they have to readjust their business strategies by creating new manufacturing processes and establishing new business networks through new technological approaches. In order to compete with big enterprises, these partnerships aim the sharing of resources, knowledge and strategies to boost the sector’s business consolidation through the creation of dynamic manufacturing networks. To facilitate such demand, it is proposed the development of a centralized information system, which allows enterprises to select and create dynamic manufacturing networks that would have the capability to monitor all the manufacturing process, including the assembly, packaging and distribution phases. Even the networking partners that come from the same area have multi and heterogeneous representations of the same knowledge, denoting their own view of the domain. Thus, different conceptual, semantic, and consequently, diverse lexically knowledge representations may occur in the network, causing non-transparent sharing of information and interoperability inconsistencies. The creation of a framework supported by a tool that in a flexible way would enable the identification, classification and resolution of such semantic heterogeneities is required. This tool will support the network in the semantic mapping establishments, to facilitate the various enterprises information systems integration

Agent ontology alignment repair through dynamic epistemic logic

vandenberg2020aInternational audienceOntology alignments enable agents to communicate while preserving heterogeneity in their information. Alignments may not be provided as input and should be able to evolve when communication fails or when new information contradicting the alignment is acquired. In the Alignment Repair Game (ARG) this evolution is achieved via adaptation operators. ARG was evaluated experimentally and the experiments showed that agents converge towards successful communication and improve their alignments. However, whether the adaptation operators are formally correct, complete or redundant is still an open question. In this paper, we introduce a formal framework based on Dynamic Epistemic Logic that allows us to answer this question. This framework allows us (1) to express the ontologies and alignments used, (2) to model the ARG adaptation operators through announcements and conservative upgrades and (3) to formally establish the correctness, partial redundancy and incompleteness of the adaptation operators in ARG

An interaction-based approach to semantic alignment

atencia2012aInternational audienceWe tackle the problem of semantic heterogeneity in the context of agent communication and argue that solutions based solely on ontologies and ontology matching do not capture adequately the richness of semantics as it arises in dynamic and open multiagent systems. Current solutions to the semantic heterogeneity problem in distributed systems usually do not address the contextual nuances of the interaction underlying an agent communication. The meaning an agent attaches to its utterances is, in our view, very relative to the particular dialogue in which it may be engaged, and that the interaction model specifying its dialogical structure and its unfolding should not be left out of the semantic alignment mechanism. In this article we provide the formal foundation of a novel, interaction-based approach to semantic alignment, drawing from a mathematical construct inspired from category theory that we call the communication product. In addition, we describe a simple alignment protocol which, combined with a probabilistic matching mechanism, endows an agent with the capacity of bootstrapping --by repeated successful interaction-- the basic semantic relationship between its local vocabulary and that of another agent. We have also implemented the alignment technique based on this approach and prove its viability by means of an abstract experimentation and a thorough statistical analysis

An interaction-based approach to semantic alignment

We tackle the problem of semantic heterogeneity in the context of agent communication and argue that solutions based solely on ontologies and ontology matching do not capture adequately the richness of semantics as it arises in dynamic and open multiagent systems. Current solutions to the semantic heterogeneity problem in distributed systems usually do not address the contextual nuances of the interaction underlying an agent communication. The meaning an agent attaches to its utterances is, in our view, very relative to the particular dialogue in which it may be engaged, and that the interaction model specifying its dialogical structure and its unfolding should not be left out of the semantic alignment mechanism. In this article we provide the formal foundation of a novel, interaction-based approach to semantic alignment, drawing from a mathematical construct inspired from category theory that we call the communication product. In addition, we describe a simple alignment protocol which, combined with a probabilistic matching mechanism, endows an agent with the capacity of bootstrapping - by repeated successful interaction - the basic semantic relationship between its local vocabulary and that of another agent. We have also implemented the alignment technique based on this approach and prove its viability by means of an abstract experimentation and a thorough statistical analysis. © 2011 Elsevier B.V. All rights reserved.This work was supported under Grant 2009-SGR-1434 from the Generalitat de Catalunya, and under the projects Agreement Technologies (CONSOLIDER INGENIO 2010 CSD2007-00022) and CBIT (TIN2010-16306) funded by the Spanish Ministry of Science and Innovation.Peer Reviewe