Decentralized Refinement Planning and Acting
| Multi-agent systems Decentralized cooperative agents Hierarchical task refinement Integrating acting and planning Operational models Online planning |
PaperID: 143
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poster
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| We describe Dec-RPAE, a system for decentralized multi-agent acting and planning in partially observable and nondeterministic environments. The system includes both an acting component and an online planning component. The acting component is similar to RAE, a well-known acting engine, but incorporates changes that enable it to be used by autonomous agents working independently in a collaborative setting. Agents can communicate with each other to exchange information about their states, tasks, goals and plans in order to cooperatively succeed in missions. Communication is not always guaranteed or free, and agents need to reason about strategies to achieve optimal success and efficiency in missions under various constraints and with possibility of failures. Each agent runs a local copy of Dec-RPAE, with a set of hierarchical refinement methods using operational models that specify various ways to accomplish its designated tasks. To perform actions, the agent uses Dec-RPAE's acting component to execute the methods in the agent's environment. To advise the acting component on which method to execute, the planning component repeatedly does Monte Carlo simulations of the methods to estimate their potential outcomes. Our experimental results demonstrate that this online planning process is useful for improving the agents’ performance in cooperative missions. | |
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