Extending the RoboCup Rescue to Support Stigmergy: Experiments and Results

Social insects have inspired researches in computer sciences as well asengineers to develop models for coordination and cooperation in multiagent systems.One example of these models is the model of stigmergy. In this model agents useindirect communication (comunication trough the environment) in order to coordinateactions. The RoboCup Rescue simulator is used as a testbed to evaluate this modelin a real world considering a highly constrained scenario of an earthquake. This pa-per investigates the feasibility of using stigmergy in the RoboCup Rescue and theimprovements of performance can be obtained. We extended the RoboCup Rescueenvironment to enable the use of stigmergy by the agents. We compared the results ofa multiagent system that uses stigmergy against two other approaches: a multiagentsystem that uses a greedy strategy and no communication, and a multiagent systemwhere agents communicate via direct messages. Experimental results shown that theuse of stigmergy leads to an improvement on agents’ performance by 9.02% to 38.6%if comparing to the system with no communication and can be statistically equivalentto the system which uses messages, depending on the scenario

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

06251 Abstracts Collection -- Multi-Robot Systems: Perception, Behaviors, Learning, and Action

From 19.06.06 to 23.06.06, the Dagstuhl Seminar 06251 ``Multi-Robot Systems: Perception, Behaviors, Learning, and Action\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

TOKEN-BASED APPROACH FOR SCALABLE TEAMCOORDINATION

To form a cooperative multiagent team, autonomous agents are required to harmonize activities and make the best use of exclusive resources to achieve their common goal. In addition, to handle uncertainty and quickly respond to external environmental events, they should share knowledge and sensor in formation. Unlike small team coordination, agents in scalable team must limit the amount of their communications while maximizing team performance. Communication decisions are critical to scalable-team coordination because agents should target their communications, but these decisions cannot be supported by a precise model or by complete team knowledge.The hypothesis of my thesis is: local routing of tokens encapsulating discrete elements of control, based only on decentralized local probability decision models, will lead to efficient scalable coordination with several hundreds of agents. In my research, coordination controls including all domain knowledge, tasks and exclusive resources are encapsulated into tokens. By passing tokens around, agents transfer team controls encapsulated in the tokens. The team benefits when a token is passed to an agent who can make use of it, but communications incur costs. Hence, no single agent has sole responsible over any shared decision. The key problem lies in how agents make the correct decisions to target communications and pass tokens so that they will potentially benefit the team most when considering communication costs.My research on token-based coordination algorithm starts from the investigation of random walk of token movement. I found a little increase of the probabilities that agents make the right decision to pass a token, the overall efficiency of the token movement could be greatly enhanced. Moreover, if token movements are modeled as a Markov chain, I found that the efficiency of passing tokens could be significantly varied based on different network topologies.My token-based algorithm starts at the investigation of each single decision theoretic agents. Although under the uncertainties that exist in large multiagent teams, agents cannot act optimal, it is still feasible to build a probability model for each agents to rationally pass tokens. Specifically, this decision only allow agent to pass tokens over an associate network where only a few of team members are considered as token receiver.My proposed algorithm will build each agent's individual decision model based on all of its previously received tokens. This model will not require the complete knowledge of the team. The key idea is that I will make use of the domain relationships between pairs of coordination controls. Previously received tokens will help the receiver to infer whether the sender could benefit the team if a related token is received. Therefore, each token is used to improve the routing of other tokens, leading to a dramatic performance improvement when more tokens are added. By exploring the relationships between different types of coordination controls, an integrated coordination algorithm will be built, and an improvement of one aspect of coordination will enhance the performance of the others

Practical Verification of Decision-Making in Agent-Based Autonomous Systems

We present a verification methodology for analysing the decision-making component in agent-based hybrid systems. Traditionally hybrid automata have been used to both implement and verify such systems, but hybrid automata based modelling, programming and verification techniques scale poorly as the complexity of discrete decision-making increases making them unattractive in situations where complex log- ical reasoning is required. In the programming of complex systems it has, therefore, become common to separate out logical decision-making into a separate, discrete, component. However, verification techniques have failed to keep pace with this devel- opment. We are exploring agent-based logical components and have developed a model checking technique for such components which can then be composed with a sepa- rate analysis of the continuous part of the hybrid system. Among other things this allows program model checkers to be used to verify the actual implementation of the decision-making in hybrid autonomous systems