Comparing behavior in agent modelling task

Proceeding of: IADIS International Conference Applied Computing 2006. February 25-28, 2006, San Sebastian, Spain.Reprint from a paper published in the Proceedings of the IADIS International Conference AC 2006In multi-agent system, agents have to analyze several features in order to adapt their behavior to the current situation. This extracted information is usually related to the environment and other agents influence. In this paper we present a method that compare two different agent models in order to extract the qualitative differences between them. This proposed comparative method captures several features of the two agent models and model them considering its behavior.Publicad

A comparing method of two team behaviours in the simulation coach competition

Proceeding of: Third International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2006, Tarragona, Spain, April 3-5, 2006.The main goal of agent modelling is to extract and represent the knowledge about the behaviour of other agents. Nowadays, modelling an agent in multi-agent systems is increasingly becoming more complex and significant. Also, robotic soccer domain is an interesting environment where agent modelling can be used. In this paper, we present an approach to classify and compare the behaviour of a multi-agent system using a coach in the soccer simulation domain of the RoboCup.Publicad

A Comparing Method of Two Team Behaviours in the Simulation Coach Competition

Proceeding of: Third International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2006, Tarragona, Spain, April 3-5, 2006.The main goal of agent modelling is to extract and represent the knowledge about the behaviour of other agents. Nowadays, modelling an agent in multi-agent systems is increasingly becoming more complex and significant. Also, robotic soccer domain is an interesting environment where agent modelling can be used. In this paper, we present an approach to classify and compare the behaviour of a multi-agent system using a coach in the soccer simulation domain of the RoboCup.Publicad

CAOS Coach 2006 Simulation Team: An Opponent Modelling Approach

Agent technology represents a very interesting new means for analyzing, designing and building complex software systems. Nowadays, agent modelling in multi-agent systems is increasingly becoming more complex and significant. RoboCup Coach Competition is an exciting competition in the RoboCup Soccer League and its main goal is to encourage research in multii-agent modelling. This paper describes a novel method used by the team CAOS (CAOS Coach 2006 Simulation Team) in this competition. The objective of the team is to model successfully the behaviour of a multi-agent system

Declarative programming for agent applications

This paper introduces the execution model of a declarative programming language intended for agent applications. Features supported by the language include functional and logic programming idioms, higher-order functions, modal computation, probabilistic computation, and some theorem-proving capabilities. The need for these features is motivated and examples are given to illustrate the central ideas

Predicting opponent actions by bbservation

In competitive domains, the knowledge about the opponent can give players a clear advantage. This idea lead us in the past to propose an approach to acquire models of opponents, based only on the observation of their input-output behavior. If opponent outputs could be accessed directly, a model can be constructed by feeding a machine learning method with traces of the opponent. However, that is not the case in the Robocup domain. To overcome this problem, in this paper we present a three phases approach to model low-level behavior of individual opponent agents. First, we build a classifier to label opponent actions based on observation. Second, our agent observes an opponent and labels its actions using the previous classifier. From these observations, a model is constructed to predict the opponent actions. Finally, the agent uses the model to anticipate opponent reactions. In this paper, we have presented a proof-of-principle of our approach, termed OMBO (Opponent Modeling Based on Observation), so that a striker agent can anticipate a goalie. Results show that scores are significantly higher using the acquired opponentrsquos model of actions.Publicad

Distributed knowledge bases : A proposal for argumentation-based semantics with cooperation

O objectivo principal desta dissertação é definir um ambiente de negociação, baseada em argumentação, para bases de conhecimento distribuídas. As bases de conhecimentos são modeladas sobre um ambiente multiagente tal que cada agente possui uma base de conhecimento própria. As bases de conhecimento dos diversos agentes podem ser independentes ou podem incluir conhecimentos comuns. O requisito mínimo para haver negociação num ambiente multiagente é que os agentes tenham a capacidade de fazer propostas, que poderão ser aceites ou rejeitadas. Numa abordagem mais sofisticada, os agentes poderão responder com contra-propostas, com o intuito de alterar aspectos insatisfatórios da pro­ posta original. Um tipo ainda mais elaborado de negociação será o baseado em argumentação. A metáfora da argumentação parece ser adequada à modelação de situações em que os diferentes agentes interagem com o propósito de determinar o significado das crenças comuns. Numa negociação baseada em argumentação, as (contra­) propostas de um agente podem ser acompanhadas de argumentos a favor da sua aceitação. Um agente poderá, então, ter um argumento aceitável para uma sua crença, se conseguir argumentar com sucesso contra os argumentos, dos outros agentes, que o atacam. Assim, as crenças de um agente caracterizam-se pela relação entre os argumentos "internos" que sustentam suas crenças, e os argumentos "externos" que sustentam crenças contraditórias de outros agentes. Portanto, o raciocínio argumentativo baseia-se na "estabilidade externa" dos argumentos aceitáveis do conjunto de agentes. Neste trabalho propõe-se uma negociação baseada em argumentação em que, para chegarem a um consenso quanto ao conhecimento comum, os agentes constroem argumentos que sustentam as suas crenças ou que se opõem aos argumentos dos agentes que as contradizem. Além disso, esta proposta lida com conhecimento incompleto (i.e., argumentos parciais) pela definição de um processo de cooperação que permite completar tal conhecimento. Assim, a negociação entre agentes é um processo argumentativo-cooperativo, em que se podem alternar os argumentos contra e a favor das crenças de um agente. Para a formação das suas crenças, a cada agente Ag está associado um conjunto Cooperate de agentes com quem coopera e um outro Argue de agentes contra quem argumenta. A negociação proposta permite a modelação de bases de conhecimento hierárquicas, representando, por exemplo, a estrutura de uma organização ou uma taxonomia nalgum domínio, e de ambientes multi-agente em que cada agente representa o conhecimento referente a um determinado período de tempo. Um agente também pode ser inquirido sobre a verdade de uma crença, dependendo a resposta do agente em questão e de quais os agentes que com ele cooperam e que a ele se opõem. Essa resposta será, no entanto, sempre consistente/ paraconsistente com as bases de conhecimento dos agentes envolvidos. Esta dissertação propõe semânticas (declarativa e operacional) da argumentação numa base de conhecimento de um agente. Partindo destas, propõe, também, semântica declarativa da negociação baseada em argumentação num ambiente multi-agente. ⓿⓿⓿ ABSTRACT: The main objective of this dissertation is to define an argumentation-based negotiation framework for distributed knowledge bases. Knowledge bases are modelling over a multi-agent setting such that each agent possibly has an independent or overlapping knowledge base. The minimum requirement for a multi-agent setting negotiation is that agents should be able to make proposals which can then either be accepted or rejected. A higher level of sophistication occurs when recipients do not just have the choice of accepting or rejecting proposals, but have the option of making counter offers to alter aspects of the proposal which are unsatisfactory. An even more elaborate kind of negotiation is argumentation-based. The argumentation metaphor seems to be adequate for modelling situations where different agents argue in order to determine the meaning of common beliefs. ln an argumentation-based negotiation, the agents are able to send justifications or arguments along with (counter) proposals indicating why they should be accepted. An argument for an agent's belief is acceptable if the agent can argue successfully against attacking arguments from other agents. Thus, agent's beliefs are characterized by the relation between its "internal" arguments supporting its beliefs and the "external" arguments supporting the contradictory beliefs of other agents. So, in a certain sense, argumentative reasoning is based on the "external stability" of acceptable arguments in the multi-agent setting. This dissertation proposes that agents evaluate arguments to obtain a consensus about a common knowledge by both proposing arguments or trying to build opposing arguments against them. Moreover, this proposal deals with incomplete knowledge (i.e. partial arguments) and so a cooperation process grants arguments to achieve knowledge completeness. Therefore, a negotiation of an agent's belief is seen as an argumentation-based process with cooperation; both cooperation and argumentation are seen as interlaced processes. Furthermore, each agent Ag has both set Argue of argumentative agents and set Cooperate of cooperative agents; every Ag must reach a consensus on its arguments with agents in Argue, and Ag may ask for arguments from agents in Cooperate to complete its partial arguments. The argumentation-based negotiation proposal allows the modelling a hierarchy of knowledge bases representing, for instance, a business's organization or a taxonomy of some subject, and also an MAS where each agent represents "acquired knowledge" in a different period of time. Furthermore, any agent in an MAS can be queried regarding the truth value of some belief. It depends on from which agent such a belief is inferred, and also what the specification in both Argue and Cooperate is, given the overall agents in the MAS. However, such an answer will always be consistent/paraconsistent with the agents' knowledge base involved. This dissertation proposes a (declarative and operational) argumentation semantics for an agent's knowledge base. Furthermore, it proposes a declarative argumentation-based negotiation semantics for a multi-agent setting, which uses most of the definitions from the former semantics