Distributed and Multi-Agent Planning

Artificial Intelligence, 2011

We propose an online algorithm for planning under uncertainty in multi-agent settings modeled as DEC-POMDPs. The algorithm helps overcome the high computational complexity of solving such problems offline. The key challenges in decentralized operation are to maintain coordinated behavior with little or no communication and, when communication is allowed, to optimize value with minimal communication. The algorithm addresses these challenges by generating identical conditional plans based on common knowledge and communicating only when history inconsistency is detected, allowing communication to be postponed when necessary. To be suitable for online operation, the algorithm computes good local policies using a new and fast local search method implemented using linear programming. Moreover, it bounds the amount of memory used at each step and can be applied to problems with arbitrary horizons. The experimental results confirm that the algorithm can solve problems that are too large for the best existing offline planning algorithms and it outperforms the best online method, producing much higher value with much less communication in most cases. The algorithm also proves to be effective when the communication channel is imperfect (periodically unavailable). These results contribute to the scalability of decision-theoretic planning in multi-agent settings.

2012

Multi-agent planning (MAP) approaches have been typically conceived for independent or loosely-coupled problems to enhance the benefits of distributed planning between autonomous agents as solving this type of problems require less coordination between the agents' sub-plans. However, when it comes to tightlycoupled agents' tasks, MAP has been relegated in favour of centralized approaches and little work has been done in this direction. In this paper, we present a general-purpose MAP capable to efficiently handle planning problems with any level of coupling between agents. We propose a cooperative refinement planning approach, built upon the partial-order planning paradigm, that allows agents to work with incomplete information and to have incomplete views of the world, i.e. being ignorant of other agents' information, as well as maintaining their own private information. We show various experiments to compare the performance of our system with a distributed CSP-based MAP approach over a suite of problems. ECAI 2012 Luc De Raedt et al. (Eds.)

2021

Collaborative Multi-Agent Planning (MAP) problems with uncertainty and partial observability are often modeled as Dec-POMDPs. Yet, in deterministic domains, Qualitative Dec-POMDPs can scale up to much larger problem sizes. The best current QDec solver (QDec-FP) reduces MAP problems to multiple single-agent problems. In this paper we describe a planner that uses richer information about agents’ knowledge to improve upon QDec-FP. With this change, the planner not only scales up to larger problems with more objects, but it can also support signaling, where agents signal information to each other by changing the state of the world.

arXiv: Artificial Intelligence, 2014

Online, sample-based planning algorithms for POMDPs have shown great promise in scaling to problems with large state spaces, but they become intractable for large action and observation spaces. This is particularly problematic in multiagent POMDPs where the action and observation space grows exponentially with the number of agents. To combat this intractability, we propose a novel scalable approach based on sample-based planning and factored value functions that exploits structure present in many multiagent settings. This approach applies not only in the planning case, but also the Bayesian reinforcement learning setting. Experimental results show that we are able to provide high quality solutions to large multiagent planning and learning problems.

Online, sample-based planning algorithms for POMDPs have shown great promise in scaling to problems with large state spaces, but they become intractable for large action and observation spaces. This is particularly problematic in multiagent POMDPs where the action and observation space grows exponentially with the number of agents. To combat this intractability, we propose a novel scalable approach based on sample-based planning and factored value functions that exploits structure present in many multiagent settings. This approach applies not only in the planning case, but also in the Bayesian reinforcement learning setting. Experimental results show that we are able to provide high quality solutions to large multiagent planning and learning problems.

Multi-Robot Systems. From Swarms to Intelligent Automata Volume III, 2005

Although the presence of free communication reduces the complexity of multi-agent POMDPs to that of single-agent POMDPs, in practice, communication is not free and reducing the frequency of communication is often desirable. We present a novel approach for using centralized "single-agent" policies in decentralized multi-agent systems by maintaining and reasoning over the collection of possible joint beliefs of the team. We describe how communication can be used to integrate local observations into the team belief as needed to improve performance, and show both experimentally and through a detailed example how our approach minimizes communication while improving the performance of distributed execution.

2011 IEEE International Conference on Robotics and Automation, 2011

We address the problem of planning in the presence of uncertainty and constraints for teams of unmanned vehicles. The problem is formulated as a Constrained Markov Decision Process (C-MDP). We allow for plans with a non-zero but bounded probability of violating constraints, a quantity that we define as risk and provide a solution technique that keeps the risk below a specified threshold while optimizing reward. We also use the decoupling between the dynamics of individual agents to assume transition independence and use this assumption to reduce the complexity of the problem. We provide representative simulation results to show that our technique achieves high reward while keeping risk bounded.

2006

Distributed Partially Observable Markov Decision Problems (Distributed POMDPs) are evolving as a popular approach for modeling multiagent systems, and many different algorithms have been proposed to obtain locally or globally optimal policies. Unfortunately, most of these algorithms have either been explicitly designed or experimentally evaluated assuming knowledge of a starting belief point, an assumption that often does not hold in complex, uncertain domains. Instead, in such domains, it is important for agents to explicitly plan over continuous belief spaces. This paper provides a novel algorithm to explicitly compute finite horizon policies over continuous belief spaces, without restricting the space of policies. By marrying an efficient single-agent POMDP solver with a heuristic distributed POMDP policy-generation algorithm, locally optimal joint policies are obtained, each of which dominates within a different part of the belief region. We provide heuristics that significantly improve the efficiency of the resulting algorithm and provide detailed experimental results. To the best of our knowledge, these are the first run-time results for analytically generating policies over continuous belief spaces in distributed POMDPs.

Autonomous Robots

This paper proposes a novel highly scalable sampling-based planning algorithm for multi-robot active information acquisition tasks in complex environments. Active information gathering scenarios include target localization and tracking, active SLAM, surveillance, environmental monitoring and others. The objective is to compute control policies for sensing robots which minimize the accumulated uncertainty of a dynamic hidden state over an a priori unknown horizon. To address this problem, we propose a new sampling-based algorithm that simultaneously explores both the robot motion space and the reachable information space. Unlike relevant sampling-based approaches, we show that the proposed algorithm is probabilistically complete, asymptotically optimal and is supported by convergence rate bounds. Moreover, we propose a novel biased sampling strategy that biases exploration towards informative areas. This allows the proposed method to quickly compute sensor policies that achieve desired levels of uncertainty in large-scale estimation tasks that may involve large sensor teams, workspaces, and dimensions of the hidden state. Extensions of the proposed algorithm to account for hidden states with no prior information are discussed. We provide extensive simulation results that corroborate the theoretical analysis and show that the proposed algorithm can address large-scale estimation tasks that are computationally challenging for existing methods.

2010

(DEC-POMDPs) are a popular planning framework for multiagent teamwork to compute (near-)optimal plans. However, these methods assume a complete and correct world model, which is often violated in real-world domains. We provide a new algorithm for DEC-POMDPs that is more robust to model uncertainty, with a focus on domains with sparse agent interactions. Our STC algorithm relies on the following key ideas: (1) reduce planning-time computation by shifting some of the burden to execution-time reasoning, (2) exploit sparse interactions between agents, and (3) maintain an approximate model of agents' beliefs. We empirically show that STC is often substantially faster to existing DEC-POMDP methods without sacrificing reward performance.