Toktam Ebadi

A framework for facilitating cooperation in multi-agent systems

This work introduces a multi-agent framework that facilitates cooperation in multi-agent robotic systems. It uses a layered approach based on Coloured Petri Nets for modelling complex, concurrent conversations among agents. In this approach each agent employs a Coloured Petri Net model that allows agents to follow a plan specifying their interactions. It also allows programmers to plan for the concurrent feature of the conversation and make sure that all possible states of the problem space are considered. The framework assists the agents to identify and adapt different strategies for teammates and task selection dynamically. The agents can change their strategies in the course of dynamic environments to improve their performance. We have examined the performance of the agents in this framework by developing some task selection and teammate selection strategies for agents in a disaster scenario.

A Distributed and Concurrent Framework for Facilitating Cooperation in Dynamic Environments

This paper describes a framework that supports concurrent execution of multiple activities required for performing complex, cooperative tasks in dynamic environments. The framework is used for coordinating and managing the development and operation of a system of multi-threaded robotic agents that operate in a distributed spatial environment. Coloured Petri Nets are used to characterise task plans and interactions for the agents, which are situated in environments over which inter-agent communication has a limited range. We show that the multi-agent framework supports adaptive coordination to altered conditions in dynamic environments.

Finding Interaction Partners Using Attitude-Based Decision Strategies

In a multi-agent system, a single agent may not be capable of completing complex tasks. Therefore agents are required to form a team to fulfill the task requirements. In this paper an agent model is introduced that facilitates cooperation among agents. The experimental results demonstrate that the model is significantly useful in achieving cooperation under various environmental constraints. It also allows agents to adjust their teammate selection strategy according to environmental constraints.