Luis Moniz

Tax compliance in a simulated heterogeneous multi-agent society

We consider an individualised approach to agent behaviour in an application to the classical economic problem of tax compliance. Most economic theories consider homogeneous representative agent utilitarian approaches to explain the decision of complying or not with tax payment. However, a heterogeneous and individualised account of decision can be considered to explain certain apparently irrational behaviours. Ideas such as trust and peer perception may have a key influence in individual decisions, and thus transform the global results for society. In this paper, we apply the agent view of rationality to economic decisions and define a territory to be explored by agent technology and social simulations. We conclude that the multi-agent view can provide powerful results which might lead to significant economic policy implications.

Force Versus Majority: A Comparison in Convention Emergence Efficiency

In open societies such as multi-agent systems, it is important that coordination among the several actors is achieved efficiently. One economical way of capturing that aspiration is consensus: social conventions and lexicons are good examples of coordinating systems, where uniformity promotes shared expectations of behavior and shared meanings. We are particularly interested in consensus that is achieved without any central control or ruling, through decentralized mechanisms that prove to be effective, efficient, and robust. The nature of interactions and also the nature of society configurations may promote or inhibit consensual emergence. Traditionally, preference to adopt the most seen choices (the majority option) has dominated the emergence convention research in multi-agents, being analyzed along different social topologies. Recently, we have introduced a different type of interaction, based on force, where force is not defined a priori but evolves dynamically. We compare the Majority class of choice update against Force based interactions, along three dimensions: types of encounters, rules of interaction and network topologies. Our experiments show that interactions based on Force are significantly more efficient (fewer encounters) for group decision making.

DEVELOP-FPS: A First Person Shooter development tool for rule-based scripts

We present DEVELOP-FPS, a software tool specially designed for the development of First Person Shooter (FPS) players controlled by Rule Based Scripts. DEVELOP-FPS may be used by FPS developers to create, debug, maintain and compare rule base player behaviours, providing a set of useful functionalities: i) for an easy preparation of the right scenarios for game debugging and testing; ii) for controlling the game execution: users can stop and resume the game execution at any instant, monitoring and controlling every player in the game, monitoring the state of each player, their rule base activation, being able to issue commands to control their behaviour; and iii) to automatically run a certain number of game executions and collect data in order to evaluate and compare the players performance along a sufficient number of similar experiments.

MASTA'05: 3rd Workshop on Multi-Agent Systems - Theory and Applications

Multi-Agent Systems (MAS) is now one of the most relevant and attractive research areas in the field of computer science. Since 1993 the area of Multi-Agent Systems/Distributed Artificial Intelligence has been present in the EPIA conferences, both as individual tracks in the main conference and as autonomous workshops. The 3rd Workshop on Multi-Agent Systems: Theory and Applications (MASTA 2005) took place in the University of Beira Interior, Covilha, Portugal, December 6-8, 2005, as part of EPIA 2005 12th Portuguese Conference on Artificial Intelligence. Focusing on a fundamental area of research in Artificial Intelligence, the 3rd MASTA workshop was the forum for presenting and discussing the most recent and innovative work in the areas of multi-agent systems and autonomous agents. MASTA 2005 received 36 submissions from 11 countries with Portugal, Brazil and The Netherlands being the most contributing countries. Although submissions covered almost all areas in the agents and multi-agent research field, some topics of special interest were on: agent architectures, agentbased applications, agent-oriented software engineering, formal methods for modeling agent based systems, negotiation in MAS, coordination and MAS learning. Each paper was blindly reviewed by three senior program committee members. From the 36 submitted, 9 high quality full papers were selected for publication in the Springer LNCS main volume of the conference proceedings, while 10 papers were selected for the local UBI/IEEE proceedings, 6 as full papers and 4 as posters. We would like to thank all the authors who submitted their work to the workshop and enabled the success of MASTA 2005. We would also like to give a special thanks to all the members of the Program Committee who took upon most of the burden in reviewing the papers enabling its accurate evaluation.

Building Multi-Agent Societies from Description to Systems: Inter-Layer Translations

When we focus on (Distributed) Artificial Intelligence as an experimental science, it is not always clear which are the best experiments to try and how they should be conducted. It is up to the designer of the experiment the choice from a wide range of languages, models and architectures for his agents, and even from a numerous set of computational environments (namely, workbenches and testbeds).