This paper proposes FMAP (Forward Multi-Agent Planning), a fully-distributed multi-agent planning method that integrates planning and coordination. Although FMAP is specifically aimed at solving problems that require cooperation among agents, the flexibility of the domain-independent planning model allows FMAP to tackle multi-agent planning tasks of any type. In FMAP, agents jointly explore the plan space by building up refinement plans through a complete and flexible forward-chaining partial-order planner. The search is guided by @math , a novel heuristic function that is based on the concepts of Domain Transition Graph and frontier state and is optimized to evaluate plans in distributed environments. Agents in FMAP apply an advanced privacy model that allows them to adequately keep private information while communicating only the data of the refinement plans that is relevant to each of the participating agents. Experimental results show that FMAP is a general-purpose approach that efficiently solves tightly-coupled domains that have specialized agents and cooperative goals as well as loosely-coupled problems. Specifically, the empirical evaluation shows that FMAP outperforms current MAP systems at solving complex planning tasks that are adapted from the International Planning Competition benchmarks.
Finally, MAPR is a recent planner that performs goal allocation to each agent @cite_20 . Agents iteratively solve the assigned goals by extending the plan of the previous agent. In this approach, agents work under limited knowledge of the environment by obfuscating the private information in their plans. MAPR is particularly effective for loosely-coupled problems, but it cannot deal with tasks that feature specialized agents and cooperative goals since it assumes that each goal is achieved by a single agent. Section will show a comparative performance evaluation between MAPR and FMAP, our proposed approach.
@cite_20
2015886927 2394593764 2962969317 2951198288
This paper proposes FMAP (Forward Multi-Agent Planning), a fully-distributed multi-agent planning method that integrates planning and coordination. Although FMAP is specifically aimed at solving problems that require cooperation among agents, the flexibility of the domain-independent planning model allows FMAP to tackle multi-agent planning tasks of any type. In FMAP, agents jointly explore the plan space by building up refinement plans through a complete and flexible forward-chaining partial-order planner. The search is guided by h D T G , a novel heuristic function that is based on the concepts of Domain Transition Graph and frontier state and is optimized to evaluate plans in distributed environments. Agents in FMAP apply an advanced privacy model that allows them to adequately keep private information while communicating only the data of the refinement plans that is relevant to each of the participating agents. Experimental results show that FMAP is a general-purpose approach that efficiently solves tightly-coupled domains that have specialized agents and cooperative goals as well as loosely-coupled problems. Specifically, the empirical evaluation shows that FMAP outperforms current MAP systems at solving complex planning tasks that are adapted from the International Planning Competition benchmarks. Generating plans for a single agent has been shown to be a difficult task. If we generalize to a multi-agent setting, the problem becomes exponentially harder in general. The centralized approach where a plan is jointly generated for all agents is only possible in some applications when agents do not have private goals, actions or states. We describe in this paper an alternative approach, MAPR (Multi-Agent Planning by plan Reuse), that considers both the agents private and public information. We have been inspired by iterative Multi-Agent Planning (MAP) techniques as the one presented in [1]. MAPR first assigns a subset of public goals to each agent, while each agent might have a set of private goals also. Then, MAPR calls the first agent to provide a solution (plan) that takes into account its private and public goals. MAPR iteratively calls each agent with the solutions provided by previous agents. Each agent receives its own goals plus the goals of the previous agents. Thus, each agent solves its problem, but taking into account the previous agents solutions. Since previous solutions might consider private data, all private information from an agent is obfuscated for the next ones. Since each agent receives the plan from the previous agent that implicitly considers the solutions to all previous agents, instead of starting the search from scratch, it can also reuse the previous whole plan or only a subset of the actions. Experiments show that MAPR outperforms in several orders of magnitude state-of-the-art techniques in the tested domains. The multi-agent path-finding (MAPF) problem has recently received a lot of attention. However, it does not capture important characteristics of many real-world domains, such as automated warehouses, where agents are constantly engaged with new tasks. In this paper, we therefore study a lifelong version of the MAPF problem, called the multi-agent pickup and delivery (MAPD) problem. In the MAPD problem, agents have to attend to a stream of delivery tasks in an online setting. One agent has to be assigned to each delivery task. This agent has to first move to a given pickup location and then to a given delivery location while avoiding collisions with other agents. We present two decoupled MAPD algorithms, Token Passing (TP) and Token Passing with Task Swaps (TPTS). Theoretically, we show that they solve all well-formed MAPD instances, a realistic subclass of MAPD instances. Experimentally, we compare them against a centralized strawman MAPD algorithm without this guarantee in a simulated warehouse system. TP can easily be extended to a fully distributed MAPD algorithm and is the best choice when real-time computation is of primary concern since it remains efficient for MAPD instances with hundreds of agents and tasks. TPTS requires limited communication among agents and balances well between TP and the centralized MAPD algorithm. Many multi-agent coordination problems can be represented as DCOPs. Motivated by task allocation in disaster response, we extend standard DCOP models to consider uncertain task rewards where the outcome of completing a task depends on its current state, which is randomly drawn from unknown distributions. The goal of solving this problem is to find a solution for all agents that minimizes the overall worst-case loss. This is a challenging problem for centralized algorithms because the search space grows exponentially with the number of agents and is nontrivial for standard DCOP algorithms we have. To address this, we propose a novel decentralized algorithm that incorporates Max-Sum with iterative constraint generation to solve the problem by passing messages among agents. By so doing, our approach scales well and can solve instances of the task allocation problem with hundreds of agents and tasks.