Alejandro Torreño

FMAP: Distributed cooperative multi-agent planning

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 hDTG, 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.

A Flexible Coupling Approach to Multi-Agent Planning under Incomplete Information

Multi-agent planning (MAP) approaches are typically oriented at solving loosely coupled problems, being ineffective to deal with more complex, strongly related problems. In most cases, agents work under complete information, building complete knowledge bases. The present article introduces a general-purpose MAP framework designed to tackle problems of any coupling levels under incomplete information. Agents in our MAP model are partially unaware of the information managed by the rest of agents and share only the critical information that affects other agents, thus maintaining a distributed vision of the task. Agents solve MAP tasks through the adoption of an iterative refinement planning procedure that uses single-agent planning technology. In particular, agents will devise refinements through the partial-order planning paradigm, a flexible framework to build refinement plans leaving unsolved details that will be gradually completed by means of new refinements. Our proposal is supported with the implementation of a fully operative MAP system and we show various experiments when running our system over different types of MAP problems, from the most strongly related to the most loosely coupled.

Cooperative Multi-Agent Planning: A Survey

Cooperative multi-agent planning (MAP) is a relatively recent research field that combines technologies, algorithms, and techniques developed by the Artificial Intelligence Planning and Multi-Agent Systems communities. While planning has been generally treated as a single-agent task, MAP generalizes this concept by considering multiple intelligent agents that work cooperatively to develop a course of action that satisfies the goals of the group. This article reviews the most relevant approaches to MAP, putting the focus on the solvers that took part in the 2015 Competition of Distributed and Multi-Agent Planning, and classifies them according to their key features and relative performance.

An architecture for defeasible-reasoning-based cooperative distributed planning

Cooperation plays a fundamental role in distributed planning, in which a team of distributed intelligent agents with diverse preferences, abilities and beliefs must cooperate during the planning process to achieve a set of common goals. This paper presents a MultiAgent Planning and Argumentation (MAPA) architecture based on a multiagent partial order planning paradigm using argumentation for communicating agents. Agents use an argumentation-based defeasible reasoning to support their own beliefs and refute the beliefs of the others according to their knowledge. In MAPA, actions and arguments may be proposed by different agents to enforce some goal, if their conditions are known to apply and arguments are not defeated by other arguments applying. In order to plan for these goals, agents start a stepwise dialogue consisting of exchanges of plan proposals to satisfy this open goal, and they evaluate each plan proposal according to the arguments put forward for or against it. After this, an agreement must be reached in order to select the next plan to be refined.

Reaching a common agreement discourse universe on multi-agent planning

Multi-Agent Planning (MAP) is the problem of having a group of agents working together to solve a problem that requires a collective effort When coordination on a MAP system is done through negotiation, agents must share a common ontology In this paper we propose a mechanism to reach a shared ontology through the definition of a common information model.

A better-response strategy for self-interested planning agents

When self-interested agents plan individually, interactions that prevent them from executing their actions as planned may arise. In these coordination problems, game-theoretic planning can be used to enhance the agents’ strategic behavior considering the interactions as part of the agents’ utility. In this work, we define a general-sum game in which interactions such as conflicts and congestions are reflected in the agents’ utility. We propose a better-response planning strategy that guarantees convergence to an equilibrium joint plan by imposing a tax to agents involved in conflicts. We apply our approach to a real-world problem in which agents are Electric Autonomous Vehicles (EAVs). The EAVs intend to find a joint plan that ensures their individual goals are achievable in a transportation scenario where congestion and conflicting situations may arise. Although the task is computationally hard, as we theoretically prove, the experimental results show that our approach outperforms similar approaches in both performance and solution quality.

FMAP: A platform for the development of distributed multi-agent planning systems

Abstract The development of cooperative Multi-Agent Planning (MAP) solvers in a distributed context encompasses the design and implementation of decentralized algorithms that make use of multi-agent communication protocols. In this paper, we present FMAP , a platform aimed at developing distributed MAP solvers such as MAP-POP , FMAP and MH-FMAP , among others.

Parallel heuristic search in forward partial-order planning

This work has been partially supported by the Spanish MINECO project TIN2014-55637-C2-2-R and cofounded by FEDER.This work has been partially supported by the Spanish MINECO project TIN2014-55637-C2-2-R and cofounded by FEDER.