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On Computing Universal Plans for Partially Observable Multi-Agent Path Finding (2305.16203v3)

Published 25 May 2023 in cs.MA and cs.AI

Abstract: Multi-agent routing problems have drawn significant attention nowadays due to their broad industrial applications in, e.g., warehouse robots, logistics automation, and traffic control. Conventionally, they are modelled as classical planning problems. In this paper, we argue that it is beneficial to formulate them as universal planning problems. We therefore propose universal plans, also known as policies, as the solution concepts, and implement a system called ASP-MAUPF (Answer Set Programming for Multi-Agent Universal Plan Finding) for computing them. Given an arbitrary two-dimensional map and a profile of goals for the agents, the system finds a feasible universal plan for each agent that ensures no collision with others. We use the system to conduct some experiments, and make some observations on the types of goal profiles and environments that will have feasible policies, and how they may depend on agents' sensors. We also demonstrate how users can customize action preferences to compute more efficient policies, even (near-)optimal ones.

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References (40)
  1. Optimizing fixed-size stochastic controllers for POMDPs and decentralized POMDPs. Autonomous Agents and Multi-Agent Systems 21 (2010), 293–320.
  2. From multi-agent pathfinding to 3D pipe routing. In Proceedings of the International Symposium on Combinatorial Search, Vol. 11. 11–19.
  3. Policy iteration for decentralized control of Markov decision processes. Journal of Artificial Intelligence Research 34 (2009), 89–132.
  4. Aysu Bogatarkan and Esra Erdem. 2020. Explanation generation for multi-modal multi-agent path finding with optimal resource utilization using answer set programming. Theory and Practice of Logic Programming 20, 6 (2020), 974–989.
  5. A Declarative Method for Dynamic Multi-Agent Path Finding.. In GCAI. 54–67.
  6. Blai Bonet and Hector Geffner. 2011. Planning under partial observability by classical replanning: Theory and experiments. In Twenty-Second International Joint Conference on Artificial Intelligence.
  7. Multi-Train Path Finding Revisited. In Proceedings of the Symposium on Combinatorial Search (SoCS). 38–46.
  8. Paolo Ferraris and Vladimir Lifschitz. 2005. Mathematical Foundations of Answer Set Programming.. In We will show them!(1). 615–664.
  9. Counterfactual multi-agent policy gradients. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32.
  10. Embedding high-level knowledge into dqns to learn faster and more safely. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 13608–13609.
  11. Multi-shot ASP solving with clingo. Theory and Practice of Logic Programming 19, 1 (2019), 27–82.
  12. Potassco: The Potsdam answer set solving collection. Ai Communications 24, 2 (2011), 107–124.
  13. Michael Gelfond and Vladimir Lifschitz. 1988. The stable model semantics for logic programming.. In ICLP/SLP, Vol. 88. Cambridge, MA, 1070–1080.
  14. Solving sum-of-costs multi-agent pathfinding with answer-set programming. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 9867–9874.
  15. Dynamic programming for partially observable stochastic games. In AAAI, Vol. 4. 709–715.
  16. Planning and acting in partially observable stochastic domains. Artificial intelligence 101, 1-2 (1998), 99–134.
  17. Scalable Rail Planning and Replanning: Winning the 2020 Flatland Challenge. In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS). 477–485.
  18. Multi-Agent Path Finding for Large Agents. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI). 7627–7634.
  19. Scheduling and Airport Taxiway Path Planning under Uncertainty. In Proceedings of the AIAA Aviation Forum.
  20. V LIFSCHITZ. 2008. What is answer set programming?. In Proc. 23rd AAAI Conf. on Artificial Intelligence, 2008. 1594–1597.
  21. Michael L Littman. 2009. A tutorial on partially observable Markov decision processes. Journal of Mathematical Psychology 53, 3 (2009), 119–125.
  22. Multi-agent actor-critic for mixed cooperative-competitive environments. Advances in neural information processing systems 30 (2017).
  23. On the undecidability of probabilistic planning and infinite-horizon partially observable Markov decision problems. In AAAI/IAAI. Citeseer, 541–548.
  24. Victor W Marek and Miroslaw Truszczyński. 1999. Stable models and an alternative logic programming paradigm. In The Logic Programming Paradigm: a 25-Year Perspective. Springer, 375–398.
  25. Ilkka Niemelä. 1999. Logic programs with stable model semantics as a constraint programming paradigm. Annals of mathematics and Artificial Intelligence 25 (1999), 241–273.
  26. Frans A Oliehoek and Christopher Amato. 2016. A concise introduction to decentralized POMDPs. Springer.
  27. Rasmus Rendsvig and John Symons. 2021. Epistemic Logic. In The Stanford Encyclopedia of Philosophy (Summer 2021 ed.), Edward N. Zalta (Ed.). Metaphysics Research Lab, Stanford University.
  28. Primal: Pathfinding via reinforcement and imitation multi-agent learning. IEEE Robotics and Automation Letters 4, 3 (2019), 2378–2385.
  29. Marcel Schoppers. 1987. Universal Plans for Reactive Robots in Unpredictable Environments.. In IJCAI, Vol. 87. Citeseer, 1039–1046.
  30. Conflict-based search for optimal multi-agent pathfinding. Artificial Intelligence 219 (2015), 40–66.
  31. Roni Stern. 2019. Multi-agent path finding–an overview. Artificial Intelligence (2019), 96–115.
  32. Multi-agent pathfinding: Definitions, variants, and benchmarks. In Twelfth Annual Symposium on Combinatorial Search.
  33. Pavel Surynek. 2019. Unifying search-based and compilation-based approaches to multi-agent path finding through satisfiability modulo theories. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. 1177–1183.
  34. Efficient SAT approach to multi-agent path finding under the sum of costs objective. In Proceedings of the twenty-second european conference on artificial intelligence. 810–818.
  35. Answer Set Planning: A Survey. Theory and Practice of Logic Programming 23, 1 (2023), 226–298.
  36. Which MAPF Model Works Best for Automated Warehousing?. In Proceedings of the Symposium on Combinatorial Search (SoCS). 190–198.
  37. A new constraint satisfaction perspective on multi-agent path finding: Preliminary results. In 18th International Conference on Autonomous Agents and Multi-Agent Systems.
  38. Hybrid conditional planning using answer set programming. Theory and Practice of Logic Programming 17, 5-6 (2017), 1027–1047.
  39. Jingjin Yu and Steven M LaValle. 2013. Structure and intractability of optimal multi-robot path planning on graphs. In Twenty-Seventh AAAI Conference on Artificial Intelligence.
  40. Knowledge-based programs as succinct policies for partially observable domains. Artificial Intelligence 288 (2020), 103365.

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