A framework for multi-agent planning

We present a m ulti-agent approach to planning for con guration, repair and maintenance activities in networked computing environments. In this setting, several planners, represented by di erent agents, cooperate on the network in order to build plans of actions needed to achieve g i v en goals. The knowledge domain is partitioned in sub-domains and each agent i s i n c harge of managing and operating upon a given subset of resources, called the agent domain. In order to implement such distributed planning architecture we h a ve applied the agent t e c hnology to a constraint-based planner called PlanNet (Planning for the Network) 1]. The exploitation of the agent t e c hnology allows for an e cient and reliable distributed planning activity.

Proceedings of the 2nd Conference on Theoretical …, 1988

In this paper we develop a formal computational theory of high-level linguistic communication that serves as a foundation for understanding cooperative action in groups of autonomous agents. We do so by examining and describing how messages affect the planning process and thereby relating communication to the intentions of the agents. We start by developing an abstract formal theory of knowledge representation based on the concept of information. We distinguish two types of information: state information, which describes the agent's knowledge about its world (knowing that) and process information, which describes the agent's knowledge of how to achieve some goal (knowing how). These two types of information are then used to formally define the agent's representation of knowledge states including the agent's intentional states. We then Show how situations and actions are related to the knowledge states. Using these relations we define a formal situation semantics for a propositional language. Based on this semantics, a formal pragmatic interpretation of the language is defined that formally describes how any given knowledge representational state is modified by a given message. Finally, using this theory of meaning of messages or speech acts, a theory of cooperation by means of communication is described.

2014

Decentralised planning in partially observable multi-agent domains is limited by the interacting agents' incomplete knowledge of their peers, which impacts their ability to work jointly towards a common goal. In this context, communication is often used as a means of observation exchange, which helps each agent in reducing uncertainty and acquiring a more centralised view of the world. However, despite these merits, planning with communicated observations is highly sensitive to communication channel noise and synchronisation issues, e.g. message losses, delays, and corruptions. In this paper, we propose an alternative approach to partially observable uncoordinated collaboration, where agents simultaneously execute and communicate their actions to their teammates. Our method extends a state-of-the-art Monte-Carlo planner for use in multi-agent systems, where communicated actions are incorporated directly in the sampling and learning process. We evaluate our approach in a benchmark multi-agent domain, and a more complex multi-robot problem with a larger action space. The experimental results demonstrate that our approach can lead to robust collaboration under challenging communication constraints and high noise levels, even in the presence of teammates who do not use any communication.

Lecture Notes in Computer Science, 2001

Distributed problem solving involves the collective effort of multiple problems solvers to combine their knowledge, information, and capabilities so as to develop solutions to problems that each could not have solved as well (if at all) alone. The challenge in distributed problem solving is thus in marshalling the distributed capabilities in the right ways so that the problem solving activities of each agent complement the activities of the others, so as to lead efficiently to effective solutions. Thus, while working together leads to distributed problem solving, there is also the distributed problem of how to work together that must be solved. We consider that problem to be a distributed planning problem, where each agent must formulate plans for what it will do that take into account (sufficiently well) the plans of other agents. In this paper, we characterize the variations of distributed problem solving and distributed planning, and summarize some of the basic techniques that have been developed to date.

Proceedings of the 13th International Conference on Software Technologies, 2018

In multi-agent systems, agents are socially cooperated with their neighboring agents to accomplish their goals. In this paper, we propose an agent-based architecture to handle different services and tasks; in particular, we focus on individual planning and distributed task allocation. We introduce the multi-agent planning in which each agent uses the fuzzy logic technique to select the alternative plans. We also propose an effective task allocation algorithm able to manage loosely coupled distributed environments where agents and tasks are heterogeneous. We illustrate our line of thought with a Benchmark Production System used as a running example in order to explain better our contribution. A set of experiments show the efficiency of our planning approach and the performance of our distributed task allocation method.

2005

The multiagent plan coordination problem arises 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 this problem have been studied in the literature, there is as yet no agreement over a general characterization of the problem. In this paper, we describe a general framework that extends the partialorder, causal-link plan representation to the multiagent case, and that treats coordination as a form of iterative repair of plan flaws that cross agents. We show, analytically and empirically, that this algorithmic formulation can scale to the multiagent case better than can a straightforward application of the most advanced single-agent plan coordination technique, highlighting fundamental differences between single-agent and multiagent planning.

Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems - AAMAS '05, 2005

We examine whether and how the Multiagent Plan Coordination Problem, the problem of resolving interactions between the plans of multiple agents, can be cast as a Distributed Constraint Optimization Problem (DCOP). We use ADOPT, a state-of-the-art DCOP solver that can solve DCOPs in an asynchronous, parallel manner using local communication between individual computational agents. We then demonstrate how we can take advantage of novel flaw-assignment strategies and plan coordination algorithms to significantly improve the performance of ADOPT on representative coordination problems. We close with a consideration of possible advances in framing our DCOP representation of the Multiagent Plan Coordination Problem.

Information, 2020

Planning and distributed task allocation are considered challenging problems. To address them, autonomous agents called planning agents situated in a multi-agent system should cooperate to achieve planning and complete distributed tasks. We propose a solution for distributed task allocation where agents dynamically allocate the tasks while they are building the plans. We model and verify some properties using computation tree logic (CTL) with the model checker its-ctl. Lastly, simulations are performed to verify the effectiveness of our proposed solution. The result proves that it is very efficient as it requires little message exchange and computational time. A benchmark production system is used as a running example to explain our contribution.

From the 9th of November to the 14th of November 2008 the Dagstuhl Seminar 08461 Planning in Multiagent Systems was held in Schloss Dagstuhl Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The rst section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.

2011

Abstract Multiagent planning is computationally hard in the general case due to the exponential blowup in the action space induced by concurrent action of different agents. At the same time, many scenarios require the computation of plans that are strategically meaningful for selfinterested agents, in order to ensure that there would be sufficient incentives for those agents to participate in a joint plan.