Submitted by PPG Ci?ncia da Computa??o (ppgcc@pucrs.br) on 2018-05-28T12:31:15Z No. of bitstreams: 1 TABAJARA_KRAUSBURG_RODRIGUES_DIS.pdf: 4049392 bytes, checksum: 154302eff9df959cfa74d6c0faec5d4e (MD5) / Approved for entry into archive by Sheila Dias (sheila.dias@pucrs.br) on 2018-06-06T13:05:51Z (GMT) No. of bitstreams: 1 TABAJARA_KRAUSBURG_RODRIGUES_DIS.pdf: 4049392 bytes, checksum: 154302eff9df959cfa74d6c0faec5d4e (MD5) / Made available in DSpace on 2018-06-06T13:35:29Z (GMT). No. of bitstreams: 1 TABAJARA_KRAUSBURG_RODRIGUES_DIS.pdf: 4049392 bytes, checksum: 154302eff9df959cfa74d6c0faec5d4e (MD5) Previous issue date: 2018-03-26 / Esta disserta??o apresenta um estudo sobre forma??o de coaliz?es entre agentes heterog?neos para a competi??o de programa??o multiagente de 2017. Foi investigado e aplicado a forma??o de estruturas de coaliz?es entre agentes para resolver problemas log?sticos simulados sobre o mapa de uma cidade real. A fim de atingir o objetivo deste trabalho, foram integrados algoritmos formadores de coaliz?es na plataforma JaCaMo por meio de um artefato CArtAgO chamado CFArtefact. Foi utilizada a implementa??o provida pelo time SMART-JaCaMo (time participante da competi??o multiagente), para experimentar a forma??o de coaliz?es na competi??o. Tr?s abordagens foram avaliadas no dom?nio da competi??o em diferentes configura??es. A primeira abordagem utiliza somente aloca??o de tarefas para resolver o problema. A segunda e a terceira abordagem utilizam a t?cnica de forma??o de coaliz?es anteriormente ? aloca??o de tarefas; dentre estas abordagens, uma utiliza um algor?timo ?timo para resolver o problema e a outra um heur?stico. As an?lises dos experimentos realizados mostram que algor?timos formadores de coaliz?es podem melhorar a performance do time participante da competi??o quando a taxa de trabalhos gerados pelo simulador ? baixa. Entretanto, conforme a taxa de trabalhos aumenta, a abordagem que realiza somente aloca??o de tarefas obt?m um desempenho melhor quando comparada as demais. Mesmo a abordagem heur?stica tem desempenho pr?ximo ? abordagem ?tima para coaliz?es. Desta forma, ? poss?vel concluir que forma??o de coaliz?es possui grande valia para balancear os agentes para um conjunto de trabalhos que precisa ser completado. / This work focuses on coalition formation among heterogeneous agents for the 2017 multiagent programming contest. An agent is a computer system that is capable of independent action to achieve its goals. In order to increase the effectiveness of the agents, we can organise them into coalitions, in which the agents collaborate with each other to achieve individual or common goals. We investigate and apply coalition structure generation (the first activity of the coalition formation process) in simulated scenarios, specifically the 2017 contest scenario, where the agents forming a competing team cooperate to solve logistic problems simulated on the map of a real city. In order to achieve our goal, we integrate coalition formation algorithms into the JaCaMo platform by means of a CArtAgO artefact, named CFArtefact. We use the implementation of the SMART JaCaMo team for experimenting with the coalition formation approach in the contest scenario. We experiment on three approaches in the contest domain with different configurations. In the first, we use only a taskallocation mechanism, while the other approaches use an optimal coalition formation algorithm and a heuristic coalition formation algorithm. We conducted several experiments to compare the advantages of each approach. Our results show that coalition formation algorithms can improve the performance of a participating team when dealing with low job rates (i.e., how quickly new jobs are created by the simulation). However, as we increase the job rate, the approach using only task allocation has better performance. Even a heuristic coalition formation approach has close performance to the optimal one in that case. Coalition formation can play an important role when we aim to balance each group of agents to accomplish some particular goal given a larger team of cooperating agents.

Parfois dans le développement logiciel, les fautes potentielles peuvent être omises, par exemple quand la complexité cache les fautes même en présence d’un processus de développement rigoureux, ou quand en raison des contraintes en termes de coûts et de temps sur les démarches de tolérance aux fautes, des risques sont assumés.La première contribution de cette thèse est l’élaboration d’un cadre de développement pour produire des logiciels tolérants aux fautes imprévues, c.à.d. fautes qui ne sont pas couvertes dans la mise en œuvre. Nous proposons 1) l’utilisation d’une architecture multi-agent avec des agents dirigés pas des buts pour le confinement des erreurs et la récupération du système ; 2) des exigences au niveau du langage de programmation ayant pour but de limiter les fautes possibles et de localiser les zones où d’autres fautes peuvent être présentes et 3) des exigences au niveau de la plate-forme d’exécution. Nous illustrons l’approche en étudiant la conception et la mise en œuvre avec un langage de programmation adapté et sa plate-forme.La deuxième contribution concerne la manière dont les agents dirigés par des buts sont programmés. Le problème est que de nombreuses approches pour la modélisation des agents cognitifs autorisent les développeurs à entrelacer les niveaux des plans et des buts, grâce à l’adoption de nouveaux buts à l’intérieur des plans. La complexité résultante peut rendre le comportement des agents difficilement traçable. Nous proposons d’imposer une séparation claire entre les niveaux de raisonnement et d’action des agents. En contraignant le modèle d’agent nous gagnons en lisibilité, avec un bénéfice pour le processus de développement. / There are situations in software development when potential faults can be omitted, e.g. when the complexity hides faults from a rigorous development process, or when due to cost and time constraints on the fault tolerance effort, risks are assumed, either consciously or not. The first contribution of this thesis is a development framework – design, language and platform requirements – for producing software that is tolerant to unforeseen faults, i.e. faults that were not covered in the implementation. We propose (1) the use of a multi-agent architecture with goal-driven agents benefits the confinement of errors and the subsequent system recovery; (2) language requirements that constrain the programmers in order to limit the possible faults and in the same time localise the areas where other faults can be present and (3) execution platform requirements in order to ensure the desired fault tolerance properties. We illustrate the approach by discussing design and implementation using an adapted agent programming language and platform. The second contribution focuses on the way goal-driven agents are programmed. The issue is that many approaches to cognitive agent modelling permit the agent developers to interweave the levels of plans and goals, through the adoption of new goals inside plans. These goals will have plans of their own, and the definition can extend on many levels, with the resulting complexity rendering the agents’ behaviour difficult to trace. We thus propose imposing a clear separation between the reasoning and the acting levels of the agent. We argue that by constraining the agent model we gain in clarity therefore benefiting the development process.