Skip to main content
placeholder image

Multiagent task allocation and planning with multi-objective requirements

Conference Paper


Abstract


  • In service robot applications, planning is often integrated with task allocation. Linear Temporal Logic (LTL) as an expressive high-level formalism is widely used for task specification, and allows for formalised restrictions on temporal sequences of tasks. In multiagent planning, a Multi-Objective Markov Decision Process extends the standard model with vector rewards capturing possibly conflicting planning objectives. Such objectives include the success rates of accomplishing individual tasks, and the cost budgets for individual agents. In this paper, we consider the problem of concurrently allocating LTL task sequences to a team of agents and calculating optimal task schedulers simultaneously, satisfying cost and probability thresholds. We reduce this problem to multi-objective scheduler synthesis for a team MDP structure, whose size is linear in the number of agents. Our preliminary experiment demonstrates the scalability of our approach.

Publication Date


  • 2021

Citation


  • Robinson, T., Su, G., & Zhang, M. (2021). Multiagent task allocation and planning with multi-objective requirements. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS Vol. 3 (pp. 1616-1618).

Scopus Eid


  • 2-s2.0-85112345274

Web Of Science Accession Number


Start Page


  • 1616

End Page


  • 1618

Volume


  • 3

Abstract


  • In service robot applications, planning is often integrated with task allocation. Linear Temporal Logic (LTL) as an expressive high-level formalism is widely used for task specification, and allows for formalised restrictions on temporal sequences of tasks. In multiagent planning, a Multi-Objective Markov Decision Process extends the standard model with vector rewards capturing possibly conflicting planning objectives. Such objectives include the success rates of accomplishing individual tasks, and the cost budgets for individual agents. In this paper, we consider the problem of concurrently allocating LTL task sequences to a team of agents and calculating optimal task schedulers simultaneously, satisfying cost and probability thresholds. We reduce this problem to multi-objective scheduler synthesis for a team MDP structure, whose size is linear in the number of agents. Our preliminary experiment demonstrates the scalability of our approach.

Publication Date


  • 2021

Citation


  • Robinson, T., Su, G., & Zhang, M. (2021). Multiagent task allocation and planning with multi-objective requirements. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS Vol. 3 (pp. 1616-1618).

Scopus Eid


  • 2-s2.0-85112345274

Web Of Science Accession Number


Start Page


  • 1616

End Page


  • 1618

Volume


  • 3