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.
In the literature, there are two main approaches for solving MAP tasks like the one described in Example . MAP involves using an intermediary agent that has complete knowledge of the task. The or decentralized approach spreads the planning responsability among agents, which are in charge of interacting with each other to coordinate their local solutions, if necessary @cite_3 @cite_12 . The adoption of a centralized approach is aimed at improving the planner performance by taking advantage of the inherent structure of the MAP tasks @cite_8 @cite_15 . Centralized approaches assume a single planning entity that has complete knowledge of the task, which is rather unrealistic if the parties involved in the task have sensitive private information that they are not willing to disclose @cite_0 . In Example , the three agents involved in the task want to protect the information regarding their internal processes and business strategies, so a centralized setting is not an acceptable solution.
@cite_8 @cite_3 @cite_0 @cite_15 @cite_12
2962969317 2394593764 1697381716 2130343868
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. 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. Coordination can be required whenever multiple agents plan to achieve their individual goals independently, but might mutually benefit by coordinating their plans to avoid working at cross purposes or duplicating effort. Although variations of such problems have been studied in the literature, there is as yet no agreement over a general characterization of them. In this paper, we formally define a common coordination problem subclass, which we call the Multiagent Plan Coordination Problem, that is rich enough to represent a wide variety of multiagent coordination problems. We then describe a general framework that extends the partial-order, causal-link plan representation to the multiagent case, and that treats coordination as a form of iterative repair of plan flaws between agents. We show that this algorithmic formulation can scale to the multiagent case better than can a straightforward application of the existing plan coordination techniques, highlighting fundamental differences between our algorithmic framework and these earlier approaches. We then examine whether and how the Multiagent Plan Coordination Problem can be cast as a Distributed Constraint Optimization Problem (DCOP). We do so using ADOPT, a state-of-the-art system that can solve DCOPs in an asynchronous, parallel manner using local communication between individual computational agents. We conclude with a discussion of possible extensions of our work. In this paper a novel approach to the problem of decentralized agreement toward a common point in space in a multi-agent system is proposed. Our method allows the agents to agree on the relative location of the network centroid respect to themselves, on a common reference frame and therefore on a common heading. Using this information a global positioning system for the agents using only local measurements can be achieved. In the proposed scenario, an agent is able to sense the distance between itself and its neighbors and the direction in which it sees its neighbors with respect to its local reference frame. Furthermore only point-to-point asynchronous communications between neighboring agents are allowed thus achieving robustness against random communication failures. The proposed algorithms can be thought as general tools to locally retrieve global information usually not available to the agents.