Verifying RoboCup Teams

Pocreeding of: 5th International Workshop on Model Checking and Artificial Intelligence. MOCHART-2008, Patras, Greece, july, 21st, 2008.Verification of multi-agent systems is a challenging task due to their dynamic nature, and the complex interactions between agents. An example of such a system is the RoboCup Soccer Simulator, where two teams of eleven independent agents play a game of football against each other. In the present article we attempt to verify a number of properties of RoboCup football teams, using a methodology involving testing. To accomplish such testing in an efficient manner we use the McErlang model checker, as it affords precise control of the scheduling of the agents, and provides convenient access to the internal states and actions of the agents of the football teams.This work has been partially supported by the FP7-ICT-2007-1 project ProTest (215868), a Ramón y Cajal grant from the Spanish Ministerio de Educación y Ciencia, and the Spanish national projects TRA2007-67374-C02-02, TIN2006-15660-C02- 02 (DESAFIOS) and S-0505/TIC/0407 (PROMESAS).Publicad

Agent-Based Simulations with Beliefs and SPARQL-Based Ask-Reply Communication

Abstract. We present the result of extending an agent-based simulation framework by adding a full-fledged model of beliefs and by supporting ask-reply communication with the help of the W3C RDF query language SPARQL. Beliefs are the core component of any cognitive agent archi-tecture. They are also the basis of ask-reply communication between agents, which allows social learning. Our approach supports the concep-tual distinctions between facts and beliefs, and between sincere answers and lies

A survey on parallel and distributed Multi-Agent Systems

International audienceSimulation has become an indispensable tool for researchers to explore systems without having recourse to real experiments. Depending on the characteristics of the modeled system, methods used to represent the system may vary. Multi-agent systems are, thus, often used to model and simulate complex systems. Whatever modeling type used, increasing the size and the precision of the model increases the amount of computation, requiring the use of parallel systems when it becomes too large. In this paper, we focus on parallel platforms that support multi-agent simulations. Our contribution is a survey on existing platforms and their evaluation in the context of high performance computing. We present a qualitative analysis, mainly based on platform properties, then a performance comparison using the same agent model implemented on each platform

Using Agent JPF to Build Models for Other Model Checkers

Abstract. We describe an extension to the AJPF agent program modelchecker so that it may be used to generate models for input into other, non-agent, model-checkers. We motivate this adaptation, arguing that it improves the efficiency of the model-checking process and provides access to richer property specification languages. We illustrate the approach by describing the export of AJPF program models to Spin and Prism. In the case of Spin we also investigate, experimentally, the effect the process has on the overall efficiency of modelchecking.

Probabilistic model checking multi-agent behaviors in dispersion games using counter abstraction

Accurate analysis of the stochastic dynamics of multi-agent system is important but challenging. Probabilistic model checking, a formal technique for analysing a system which exhibits stochastic behaviors, can be a natural solution to analyse multi-agent systems. In this paper, we investigate this problem in the context of dispersion games focusing on two strategies: basic simple strategy (BSS) and extended simple strategies (ESS). We model the system using discrete-time Markov chain (DTMC) and reduce the state space of the models by applying counter abstraction technique. Two important properties of the system are considered: convergence and convergence rate. We show that these kinds of properties can be automatically analysed and verified using probabilistic model checking techniques. Better understanding of the dynamics of the strategies can be obtained compared with empirical evaluations in previous work. Through the analysis, we are able to demonstrate that probabilistic model checking technique is applicable, and indeed useful for automatic analysis and verification of multi-agent dynamics.No Full Tex

Multiagent cooperation for solving global optimization problems: an extendible framework with example cooperation strategies

This paper proposes the use of multiagent cooperation for solving global optimization problems through the introduction of a new multiagent environment, MANGO. The strength of the environment lays in itsflexible structure based on communicating software agents that attempt to solve a problem cooperatively. This structure allows the execution of a wide range of global optimization algorithms described as a set of interacting operations. At one extreme, MANGO welcomes an individual non-cooperating agent, which is basically the traditional way of solving a global optimization problem. At the other extreme, autonomous agents existing in the environment cooperate as they see fit during run time. We explain the development and communication tools provided in the environment as well as examples of agent realizations and cooperation scenarios. We also show how the multiagent structure is more effective than having a single nonlinear optimization algorithm with randomly selected initial points

Typing Multi-Agent Systems via Commitments

Abstract. This work presents an agent typing system, that differently than most of other proposals relies on notions that are typical of agent systems instead of relying on a functional approach. Specifically, we use commitments to define types. The proposed typing includes a notion of compatibility, based on subtyping, which allows for the safe substitution of agents to roles along an interaction that is ruled by a commitment-based protocol. Type checking can be done dynamically when an agent enacts a role. The proposal is implemented in the 2COMM framework and exploits Java annotations. 2COMM is based on the Agent & Artifact meta-model, exploit JADE and CArtAgO, by using CArtAgO artifacts in order to reify commitment protocols