Studies of dynamics of physical agent ecosystems

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dc.contributor Universitat de Girona. Departament d'Electrònica, Informàtica i Automàtica
dc.contributor.author Muñoz Moreno, Israel
dc.date.issued 2002-09-04
dc.identifier.isbn 846880973X
dc.identifier.other DL Gi.424-2002
dc.identifier.uri http://www.tdx.cat/TDX-0115103-140323
dc.identifier.uri http://hdl.handle.net/10803/7717
dc.identifier.uri http://hdl.handle.net/10256/4935
dc.description.abstract This thesis addresses the problem of learning in physical heterogeneous multi-agent systems (MAS) and the analysis of the benefits of using heterogeneous MAS with respect to homogeneous ones. An algorithm is developed for this task; building on a previous work on stability in distributed systems by Tad Hogg and Bernardo Huberman, and combining two phenomena observed in natural systems, task partition and hierarchical dominance. This algorithm is devised for allowing agents to learn which are the best tasks to perform on the basis of each agent's skills and the contribution to the team global performance. Agents learn by interacting with the environment and other teammates, and get rewards from the result of the actions they perform. This algorithm is specially designed for problems where all robots have to co-operate and work simultaneously towards the same goal. One example of such a problem is role distribution in a team of heterogeneous robots that form a soccer team, where all members take decisions and co-operate simultaneously. Soccer offers the possibility of conducting research in MAS, where co-operation plays a very important role in a dynamical and changing environment. For these reasons and the experience of the University of Girona in this domain, soccer has been selected as the test-bed for this research. In the case of soccer, tasks are grouped by means of roles. One of the most interesting features of this algorithm is that it endows MAS with a high adaptability to changes in the environment. It allows the team to perform their tasks, while adapting to the environment. This is studied in several cases, for changes in the environment and in the robot's body. Other features are also analysed, especially a parameter that defines the fitness (biological concept) of each agent in the system, which contributes to performance and team adaptability. The algorithm is applied later to allow agents to learn in teams of homogeneous and heterogeneous robots which roles they have to select, in order to maximise team performance. The teams are compared and the performance is evaluated in the games against three hand-coded teams and against the different homogeneous and heterogeneous teams built in this thesis. This section focuses on the analysis of performance and task partition, in order to study the benefits of heterogeneity in physical MAS. In order to study heterogeneity from a rigorous point of view, a diversity measure is developed building on the hierarchic social entropy defined by Tucker Balch. This is adapted to quantify physical diversity in robot teams. This tool presents very interesting features, as it can be used in the future to design heterogeneous teams on the basis of the knowledge on other teams.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Universitat de Girona
dc.rights ADVERTIMENT. L'accés als continguts d'aquesta tesi doctoral i la seva utilització ha de respectar els drets de la persona autora. Pot ser utilitzada per a consulta o estudi personal, així com en activitats o materials d'investigació i docència en els termes establerts a l'art. 32 del Text Refós de la Llei de Propietat Intel·lectual (RDL 1/1996). Per altres utilitzacions es requereix l'autorització prèvia i expressa de la persona autora. En qualsevol cas, en la utilització dels seus continguts caldrà indicar de forma clara el nom i cognoms de la persona autora i el títol de la tesi doctoral. No s'autoritza la seva reproducció o altres formes d'explotació efectuades amb finalitats de lucre ni la seva comunicació pública des d'un lloc aliè al servei TDX. Tampoc s'autoritza la presentació del seu contingut en una finestra o marc aliè a TDX (framing). Aquesta reserva de drets afecta tant als continguts de la tesi com als seus resums i índexs.
dc.source TDX (Tesis Doctorals en Xarxa)
dc.subject.other Multi-agent systems
dc.subject.other Aprenentatge
dc.subject.other Sistemes multiagent
dc.subject.other Learning
dc.subject.other Robots
dc.subject.other Sistemas multi-agente
dc.subject.other Aprendizaje
dc.title Studies of dynamics of physical agent ecosystems
dc.type info:eu-repo/semantics/doctoralThesis
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.contributor.director Rosa, Josep Lluís de la
dc.subject.udc 004 - Informàtica
dc.type.version info:eu-repo/semantics/publishedVersion


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