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

Defining and using ideal teammate and opponent agent models

A common challenge for agents in multiagent systems is trying to predict what other agents are going to do in the future. Such knowledge can help an agent determine which of its current action options is most likely to achieve its goals. There is a long history in adversarial game playing of using a model of an opponent which assumes that it always acts optimally. Our research extends this strategy to adversarial domains in which the agents have incomplete information, noisy sensors and actuators, and a continuous action space. We introduce “ideal-model-based behavior outcome prediction” (IMBBOP) which models the results of other agents ’ future actions in relation to their optimal actions based on an ideal world model. Our technique also includes a method for relaxing this optimality assumption. IMBBOP was a key component of our successful CMUNITED-99 simulated robotic soccer application. We define IMBBOP and illustrate its use within the simulated robotic soccer domain. We include empirical results demonstrating the effectiveness of IMBBOP

Defining and Using Ideal Teammate and Opponent Agent Models

A common challenge for agents in multiagent systems is trying to predict what other agents are going to do in the future. Such knowledge can help an agent determine which of its current action options is most likely to achieve its goals. There is a long history in adversarial game playing of using a model of an opponent which assumes that it always acts optimally. Our research extends this strategy to adversarial domains in which the agents have incomplete information, noisy sensors and actuators, and a continuous action space. We introduce ``ideal-model-based behavior outcome prediction'' (IMBBOP) which models the results of other agents' future actions in relation to their optimal actions based on an ideal world model. Our technique also includes a method for relaxing this optimality assumption. IMBBOP was a key component of our successful CMU NITED-99 simulated robotic soccer application. In this paper, we define IMBBOP and illustrate its use within the simulated robotic soccer domain. We include empirical results demonstrating the effectiveness of IMBBOP

Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems

Much research in artificial intelligence is concerned with the development of autonomous agents that can interact effectively with other agents. An important aspect of such agents is the ability to reason about the behaviours of other agents, by constructing models which make predictions about various properties of interest (such as actions, goals, beliefs) of the modelled agents. A variety of modelling approaches now exist which vary widely in their methodology and underlying assumptions, catering to the needs of the different sub-communities within which they were developed and reflecting the different practical uses for which they are intended. The purpose of the present article is to provide a comprehensive survey of the salient modelling methods which can be found in the literature. The article concludes with a discussion of open problems which may form the basis for fruitful future research.Comment: Final manuscript (46 pages), published in Artificial Intelligence Journal. The arXiv version also contains a table of contents after the abstract, but is otherwise identical to the AIJ version. Keywords: autonomous agents, multiagent systems, modelling other agents, opponent modellin