[ca] L'analogia juga un papel fonamental en la resolucio de problemes i es troba darrere de molts dels processos centrals de la capacitat cognitiva humana, fins al punt que s'ha considerat "el nucli del coneixement". El raonament analogic funciona a traves del proces de la transferencia, l'us del coneixement apres en una situacio en l'altra per a la qual no va ser destinat. El paradigma de raonament basat en casos (case-based reasoning, CBR) presenta un model molt relacionat, pero lleugerament diferent de raonament utilitzat principalment en la intel·ligencia artificial; diferent en part perque el raonament analogic se centra habitualment en la similitud estructural entre-dominis mentre que CBR te a veure amb la transferencia de solucions entre els casos semanticament similars dins d'un domini especific. En aquesta tesi, ens unim a aquests enfocaments interrelacionats de la ciència cognitiva, la psicologia i la intel·ligencia artificial, en un sistema CBR, on la recuperacio i l'adaptacio es duen a terme per l'Motor d'Associacio Estructural (SME) i son recolzats per el raonament de sentit comu integrant la informacio des de diverses bases de coneixement. Per permetre aixo, utilitzem una estructura de representacio de casos que es basa en les xarxes semantiques. Aixo ens dona un model CBR capac de recuperar i adaptar solucions de dominis que son aparentment diferents pero estructuralment molt similars, formant una de les nostres contribucions en aquest estudi. Una de les principals limitacions de la investigacio sobre els sistemes CBR sempre ha estat l'adaptacio, on la majoria de les aplicacions es van conformar amb una simple "reutilitzacio" de la solucio del cas recuperat, principalment mitjancant una adaptacio null} o adaptacio sustitucional. La dificultat de l'adaptacio es encara mes evident per al nostre cas d'inter-domini CBR utilitzant xarxes semantiques. Resoldre aquesta dificultat aplana el cami per a una contribucio igualment important d'aquesta tesi, on s'introdueix una tecnica nova d'adaptacio generativa basada en la computacio evolutiva que permet la creacio o modificacio espontania de les xarxes semantiques d'acord a les necessitats d'adaptacio CBR. Per a l'avaluacio d'aquest treball, apliquem el nostre sistema CBR al problema de la mediacio, un metode important en la resolucio de conflictes. El problema de la mediacio no es trivial i representa un molt bon exemple del mon real, en el qual podem detectar problemes estructuralment similars de dominis aparentment tan lluny com les relacions internacionals, conflictes familiars i els drets intel·lectuals.

[en] Analogy plays a fundamental role in problem solving and it lies behind many processes central to human cognitive capacity, to the point that it has been considered "the core of cognition". Analogical reasoning functions through the process of transfer, the use of knowledge learned in one situation in another for which it was not targeted. The case-based reasoning (CBR) paradigm presents a highly related, but slightly different model of reasoning mainly used in artificial intelligence, different in part because analogical reasoning commonly focuses on cross-domain structural similarity whereas CBR is concerned with transfer of solutions between semantically similar cases within one specific domain. In this dissertation, we join these interrelated approaches from cognitive science, psychology, and artificial intelligence, in a CBR system where case retrieval and adaptation are accomplished by the Structure Mapping Engine (SME) and are supported by commonsense reasoning integrating information from several knowledge bases. For enabling this, we use a case representation structure that is based on semantic networks. This gives us a CBR model capable of recalling and adapting solutions from seemingly different, but structurally very similar domains, forming one of our contributions in this study. A traditional weakness of research on CBR systems has always been about adaptation, where most applications settle for a very simple "reuse" of the solution from the retrieved case, mostly through null adaptation or substitutional adaptation. The difficulty of adaptation is even more obvious for our case of cross-domain CBR using semantic networks. Solving this difficulty paves the way to another contribution of this dissertation, where we introduce a novel generative adaptation technique based on evolutionary computation that enables the spontaneous creation or modification of semantic networks according to the needs of CBR adaptation. For the evaluation of this work, we apply our CBR system to the problem of mediation, an important method in conflict resolution. The mediation problem is non-trivial and presents a very good real world example where we can spot structurally similar problems from domains seemingly as far as international relations, family disputes, and intellectual rights.