60 research outputs found
Automated Generation of Cross-Domain Analogies via Evolutionary Computation
Analogy plays an important role in creativity, and is extensively used in
science as well as art. In this paper we introduce a technique for the
automated generation of cross-domain analogies based on a novel evolutionary
algorithm (EA). Unlike existing work in computational analogy-making restricted
to creating analogies between two given cases, our approach, for a given case,
is capable of creating an analogy along with the novel analogous case itself.
Our algorithm is based on the concept of "memes", which are units of culture,
or knowledge, undergoing variation and selection under a fitness measure, and
represents evolving pieces of knowledge as semantic networks. Using a fitness
function based on Gentner's structure mapping theory of analogies, we
demonstrate the feasibility of spontaneously generating semantic networks that
are analogous to a given base network.Comment: Conference submission, International Conference on Computational
Creativity 2012 (8 pages, 6 figures
Dispute Resolution Using Argumentation-Based Mediation
Mediation is a process, in which both parties agree to resolve their dispute
by negotiating over alternative solutions presented by a mediator. In order to
construct such solutions, mediation brings more information and knowledge, and,
if possible, resources to the negotiation table. The contribution of this paper
is the automated mediation machinery which does that. It presents an
argumentation-based mediation approach that extends the logic-based approach to
argumentation-based negotiation involving BDI agents. The paper describes the
mediation algorithm. For comparison it illustrates the method with a case study
used in an earlier work. It demonstrates how the computational mediator can
deal with realistic situations in which the negotiating agents would otherwise
fail due to lack of knowledge and/or resources.Comment: 6 page
Evolution of Ideas: A Novel Memetic Algorithm Based on Semantic Networks
This paper presents a new type of evolutionary algorithm (EA) based on the
concept of "meme", where the individuals forming the population are represented
by semantic networks and the fitness measure is defined as a function of the
represented knowledge. Our work can be classified as a novel memetic algorithm
(MA), given that (1) it is the units of culture, or information, that are
undergoing variation, transmission, and selection, very close to the original
sense of memetics as it was introduced by Dawkins; and (2) this is different
from existing MA, where the idea of memetics has been utilized as a means of
local refinement by individual learning after classical global sampling of EA.
The individual pieces of information are represented as simple semantic
networks that are directed graphs of concepts and binary relations, going
through variation by memetic versions of operators such as crossover and
mutation, which utilize knowledge from commonsense knowledge bases. In
evaluating this introductory work, as an interesting fitness measure, we focus
on using the structure mapping theory of analogical reasoning from psychology
to evolve pieces of information that are analogous to a given base information.
Considering other possible fitness measures, the proposed representation and
algorithm can serve as a computational tool for modeling memetic theories of
knowledge, such as evolutionary epistemology and cultural selection theory.Comment: Conference submission, 2012 IEEE Congress on Evolutionary Computation
(8 pages, 7 figures
De Turing als robots humanoides : passat, present i futur de la Intel·ligència Artificial
Conferència realitzada el Dissabte, 5 de Març de 2016Possiblement la lliçó més important que hem après al llarg dels 60 anys d'existència de la Intel·ligència Artificial (IA) és que el que semblava més difÃcil d'assolir, com ara diagnosticar malalties o jugar a escacs millor que els Grans Mestres, ha resultat relativament fà cil i en canvi allò que semblava més senzill, com ara reconèixer objectes del nostre entorn, encara no ho hem aconseguit. Intentarem explicar les raons d'aquesta aparent contradicció, tot fent un breu repà s del desenvolupament de la IA des de Turing fins als actuals robots humanoides i veurem perquè és tan difÃcil construir mà quines amb IA de tipus general
Answer Set Programming for Non-Stationary Markov Decision Processes
Non-stationary domains, where unforeseen changes happen, present a challenge
for agents to find an optimal policy for a sequential decision making problem.
This work investigates a solution to this problem that combines Markov Decision
Processes (MDP) and Reinforcement Learning (RL) with Answer Set Programming
(ASP) in a method we call ASP(RL). In this method, Answer Set Programming is
used to find the possible trajectories of an MDP, from where Reinforcement
Learning is applied to learn the optimal policy of the problem. Results show
that ASP(RL) is capable of efficiently finding the optimal solution of an MDP
representing non-stationary domains
Combining Invariant Features and the ALV Homing Method for Autonomous Robot Navigation Based on Panoramas
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