An Abstract Framework for Argumentation-based Negotiation

MFI’07 - Actes des Quatrièmes Journées Francophones Modèles formels de l’interactionThis paper proposes an abstract framework for argumentation-based negotiation, in which the role of argumentation is formally analyzed. The framework makes it possible to study the outcomes of an argumentation-based negotiation. It shows what an agreement is, how it is related to the theories of the agents, when it is possible, and how this can be attained by the negotiating agents in this case. It defines also the notion of concession, and shows in which situation an agent will make one, as well as how it influences the evolution of the dialogue

A Graph Theoretic Approach to Default Logic

A network representation of propositional seminormal disjunction-free default theories is presented, leading to a graph-theoretic approach to their analysis. The problem of finding an extension is proved to be equivalent to that of determining a kernel for a corresponding graph, allowing stronger complexity results as well as new conditions for the existence of extensions

Multi-agent coordination and cooperation through classical planning

Multi-agent planning is a fundamental problem in multiagent systems that has acquired a variety of meanings in the relative literature. In this paper we focus on a setting where multiple agents with complementary capabilities cooperate in order to generate non-conflicting plans that achieve their respective goals. We study two situations. In the first, the agents are able to achieve their subgoals by themselves, but they need to find a coordinated course of action that avoids harmful interactions. In the second situation, some agents may ask the assistance of others in order to achieve their goals. We formalize the two problems and present algorithms for their solution. These algorithms are based on an underlying classical planner which is used by the agents to generate their individual plans, but also to find plans that are consistent with those of the other agents. The procedures generate optimal plans under the plan length criterion. The central role that has been given to the classical planning algorithm, can be seen as an attempt to establish a stronger link between classical and multi-agent planning.

Constraint Propagation in Propositional Planning

Planning as Satisfiability is a most successful approach to optimal propositional planning. It draws its strength from the efficiency of state-of-the-art propositional satisfiability solvers, combined with the utilization of constraints that are inferred from the problem planning graph. One of the recent improvements of the framework is the addition of long-distance mutual exclusion (londex) constraints that relate facts and actions which refer to different time steps. In this paper we compare different encodings of planning as satisfiability wrt the constraint propagation they achieve in a modern SAT solver. This analysis explains some of the differences observed in the performance of different encodings, and leads to some interesting conclusions. For instance, the Blackbox encoding achieves more propagation than the one of Satplan06, and therefore is a stronger formulation of planning as satisfiability. Moreover, our investigation suggests a new more compact and stronger model for the problem. We prove that in this new formulation many of the londex constraints are redundant in the sense that they do not add anything to the constraint propagation achieved by the model. Experimental results suggest that the theoretical results obtained are practically relevant

Integer programs and valid inequalities for planning problems

Colloque avec actes et comité de lecture. internationale.International audiencePart of the recent work in AI planning is concerned with the development of algorithms that regard planning as a combinatorial search problem. The underlying representation language is basically propositional logic. While this is adequate for many domains, it is not clear if it remains so for problems that involve numerical constraints, or optimization of complex objective functions. Moreover, the propositional representation imposes restrictions on the domain knowledge that can be utilized by these approaches. In order to address these issues, we propose moving to the more expressive language of Integer Programming (IP). We show how capacity constraints can be easily encoded into linear 0-1 inequalities and how rich forms of domain knowledge can be compactly represented and computationally exploited by IP solvers. Then we introduce a novel heuristic search method based on the linear programming relaxation. Finally, we present the results of our experiments with a classical relaxation-based IP solver and a logic-based 0-1 optimizer

Extending SATPLAN to Multiple Agents

Abstract Multi-agent planning is a core issue in the multi-agent systems field. In this work we focus on the coordination of multiple agents in a setting where agents are able to achieve individual goals that may be either independent, or necessary for the achievement of a global common goal. The agents are able to generate individual plans in order to achieve their own goals, but, as they share the same environment, they need to find a coordinated course of action that avoids harmful (or negative) interactions, and benefits from positive interactions, whenever this is possible. Moreover, agents are interested in finding plans with optimal length where preference is given to the length of the joint plan. We formalize these problems in a more general way with respect to previous works and present a coordination algorithm which provides the optimal solution in the case of two agents. In this algorithm, agents use µ-SATPLAN as the underlying planner for generating individual and joint consistent plans. This planner is an extension of the well known classical planner SATPLAN, aiming to deal with negative and positive interactions and, therefore, with multi-agent planning problem. Finally we present the experimental results using the multi-agent planning problems from the domains proposed and used in classical planning, which demonstrate the effectiveness of µ-SATPLAN and the coordination algorithm.

Multi-agent Coordination and Cooperation through Classical Planning

Multi-agent planning is a fundamental problem in multiagent systems that has acquired a variety of meanings in the relative literature. In this paper we focus on a setting where multiple agents with complementary capabilities cooperate in order to generate non-conflicting plans that achieve their respective goals. We study two situations. In the first, the agents are able to achieve their subgoals by themselves, but they need to find a coordinated course of action that avoids harmful interactions. In the second situation, some agents may ask the assistance of others in order to achieve their goals. We formalize the two problems in a more general way than in previous works, and present algorithms for their solution. These algorithms are based on an underlying classical planner which is used by the agents to generate their individual plans, but also to find plans that are consistent with those of the other agents. The procedures generate optimal plans under the plan length criterion, but they can be adapted to other criteria as well. The central role that has been given to the classical planning algorithm, can be seen as an attempt to establish a stronger link between classical and multi-agent planning.