The thesis summarises thorough analysis of rules and mechanisms of Multiagent Contest for year 2020 followed by design of multiagent group which will try to succeed in a simulation of the mentioned contest with the best results. The design was created according to the Prometheus metodology from general view (scenarios, goals) to specific details (agent plans, messages). Implementation is realized through JaCaMo system, which is also briefly described in this paper. Then the pros and cons of JaCaMo system are analyzed. Lastly the implemented solution of this work is compared with other teams that participated in the mentioned agent competition this year.

Submitted by PPG Ci?ncia da Computa??o (ppgcc@pucrs.br) on 2018-03-26T18:03:05Z No. of bitstreams: 1 ARTUR_FREITAS_TES.pdf: 1489846 bytes, checksum: 61623f4503a4a916e72487f33c15e294 (MD5) / Approved for entry into archive by Tatiana Lopes (tatiana.lopes@pucrs.br) on 2018-04-06T16:46:51Z (GMT) No. of bitstreams: 1 ARTUR_FREITAS_TES.pdf: 1489846 bytes, checksum: 61623f4503a4a916e72487f33c15e294 (MD5) / Made available in DSpace on 2018-04-06T16:57:00Z (GMT). No. of bitstreams: 1 ARTUR_FREITAS_TES.pdf: 1489846 bytes, checksum: 61623f4503a4a916e72487f33c15e294 (MD5) Previous issue date: 2017-08-31 / A engenharia orientada a modelos fornece abstra??es e nota??es para melhorar a compreens?o e para apoiar a modelagem, codifica??o e verifica??o de aplica??es em dom?nios espec?ficos. As ontologias, por outro lado, fornecem defini??es formais e expl?citas de conceitualiza??es compartilhadas e permitem o uso de racioc?nio sem?ntico. Embora essas ?reas tenham sido desenvolvidas por diferentes comunidades, sinergias importantes podem ser alcan?adas quando ambas s?o combinadas. Essas vantagens podem ser exploradas no desenvolvimento de sistemas multiagentes, dada a sua complexidade e a necessidade de integrar v?rios componentes que s?o frequentemente abordados de diferentes ?ngulos. Este trabalho investiga como aplicar ontologias para engenharia de software orientada a agentes. Inicialmente, apresentamos uma nova abordagem de modelagem onde os sistemas multiagentes s?o projetados usando a ontologia OntoMAS proposta. Ent?o, descrevemos t?cnicas, implementadas em uma ferramenta, para ajudar os programadores a trazer seus conceitos em c?digo e tamb?m gerar c?digo automaticamente a partir de modelos instanciados da ontologia. V?rias vantagens podem ser obtidas a partir dessas novas abordagens para modelar e codificar sistemas multiagentes, como o racioc?nio sem?ntico para realizar infer?ncias e mecanismos de verifica??o. Mas a principal vantagem ? a linguagem de especifica??o unificada de alto n?vel (conhecimento) que permite modelar as tr?s dimens?es que est?o unidas em JaCaMo para que as especifica??es dos sistemas possam ser melhor comunicadas entre equipes em desenvolvimento. As avalia??es dessas propostas indicam que elas contribuem com os diferentes aspectos da engenharia de software orientada a agentes, como a especifica??o, verifica??o e programa??o desses sistemas. / Model-driven engineering provides abstractions and notations for improving the understanding and for supporting the modelling, coding, and verification of applications for specific domains. Ontologies, on the other hand, provide formal and explicit definitions of shared conceptualisations and enable the use of semantic reasoning. Although these areas have been developed by different communities, important synergies can be achieved when both are combined. These advantages can be explored in the development of multi-agent systems, given their complexity and the need for integrating several components that are often addressed from different angles. This work investigates how to apply ontologies for agentoriented software engineering. Initially, we present a new modelling approach where multiagent systems are designed using the proposed OntoMAS ontology. Then, we describe techniques, implemented in a tool, to help programmers bring their concepts into code and also generate code automatically from instantiated ontology models. Several advantages can be obtained from these new approaches to model and code multi-agent systems, such as semantic reasoning to carry out inferences and verification mechanisms. But the main advantage is the unified high (knowledge) level specification language that allows modelling the three dimensions that are united in the JaCaMo framework so that systems specifications can be better communicated across developing teams. The evaluations of these proposals indicate that they contribute with the different aspects of agent-oriented software engineering, such as the specification, verification, and programming of these systems.

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.