This paper explores the use of Answer Set Programming (ASP) in solving Distributed Constraint Optimization Problems (DCOPs). The paper provides the following novel contributions: (1) It shows how one can formulate DCOPs as logic programs; (2) It introduces ASP-DPOP, the first DCOP algorithm that is based on logic programming; (3) It experimentally shows that ASP-DPOP can be up to two orders of magnitude faster than DPOP (its imperative programming counterpart) as well as solve some problems that DPOP fails to solve, due to memory limitations; and (4) It demonstrates the applicability of ASP in a wide array of multi-agent problems currently modeled as DCOPs. Under consideration in Theory and Practice of Logic Programming (TPLP).
This paper presents a framework that integrates three aspects of agency: planning, for proactive behaviour, negotiation, for social behaviour and resource achievement, and control of operation, for reconciling rationality with reactivity. Agents are designed and programmed in a computational logic-based language where these aspects are accommodated in a declarative and modular way. We show how this framework can be applied to agent problems requiring negotiation and resource achievement and present some of its formal properties. The framework can be implemented based on a communication platform for agent interaction and on well-established logic programming technologies for agent reasoning.  In this paper we present an extension of logic programming (LP) that is suitable not only for the “rational” component of a single agent but also for the “reactive” component and that can encompass multidagent systems. We modify an earlier abductive proof procedure and embed it within an agent cycle. The proof procedure incorporates abduction, definitions and integrity constraints within a dynamic environment, where changes can be observed as inputs. The definitions allow rational planning behaviour and the integrity constraints allow reactive, conditiondaction type behaviour. The agent cycle provides a resourcedbounded mechanism that allows the agent’s thinking to be interrupted for the agent to record and assimilate observations as input and execute actions as output, before resuming further thinking. We argue that these extensions of LP, accommodating multidtheories embedded in a shared environment, provide the necessary multidagent functionality. We argue also that our work extends Shoham’s Agent0 and the BDI architecture. This paper explores the use of Constraint Logic Programming (CLP) as a platform for experimenting with planning problems in the presence of multiple interacting agents. The paper develops a novel constraint-based action language, B MAP, that enables the declarative description of large classes of multi-agent and multi-valued domains. B MAP supports several complex features, including combined effects of concurrent and interacting actions, concurrency control, and delayed effects. The paper presents a mapping of B MAP theories to CLP and it demonstrates the effectiveness of an implementation in SICStus Prolog on several benchmark problems. The effort is an evolution of previous research on using CLP for single-agent domains, demonstrating the flexibility of CLP technology to handle the more complex issues of multi-agency and concurrency. Multiagent planning deals with the problem of generating plans for multiple agents. It requires formalizing ways for the agents to interact and cooperate, in order to achieve their goals. One way for the agents to interact is through negotiations. Integration of negotiation in multiagent planning has not been extensively investigated and a systematic way for this task has yet to be found. We develop a generic model for negotiation in dynamic environments and apply it to generate joint-plans with negotiation for multiple agents. We identify the minimal requirements for such a model and propose a general scheme for one-to-one negotiations. This model of negotiation is instantiated to deal with dynamic knowledge of planning agents. We demonstrate how logic programming can be employed as a uniform platform to support both planning and negotiation, providing an ideal testbed for experimenting with multiagent planning with negotiations. The paper introduces a logical framework for negotiation among dishonest agents. The framework relies on the use of abductive logic programming as a knowledge representation language for agents to deal with incomplete information and preferences. The paper shows how intentionally false or inaccurate information of agents could be encoded in the agents' knowledge bases. Such disinformation can be effectively used in the process of negotiation to have desired outcomes by agents. The negotiation processes are formulated under the answer set semantics of abductive logic programming and enable the exploration of various strategies that agents can employ in their negotiation.  Abstract Multi Agent Systems (MAS) have become the key technology for decomposing complex problems in order to solve them more efficiently, or for problems distributed in nature. However, many industrial applications besides their distributed nature, also involve a large number of parameters and constraints among them, i.e. they are combinatorial. Solving such particularly hard problems efficiently requires programming tools that combine MAS technology with a programming schema that facilitates the modeling and solution of constraints. This paper presents MACLP (Multi Agent Constraint Logic Programming), a logic-programming platform for building, in a declarative way, multi agent systems with constraint-solving capabilities. MACLP extends CSPCONS, a logic programming system that permits distributed program execution through communicating sequential Prolog processes with constraints, by providing all the necessary facilities for communication between agents. These facilities abstract from the programmer all the low-level details of the communication and allow him to focus on the development of the agent itself. The paper presents a knowledge representation formalism, in the form of a high-level Action Description Language (ADL) for multi-agent systems, where autonomous agents reason and act in a shared environment. Agents are autonomously pursuing individual goals, but are capable of interacting through a shared knowledge repository. In their interactions through shared portions of the world, the agents deal with problems of synchronization and concurrency; the action language allows the description of strategies to ensure a consistent global execution of the agents’ autonomously derived plans. A distributed planning problem is formalized by providing the declarative specications of the portion of the problem pertaining a single agent. Each of these specications is executable by a stand-alone CLP-based planner. The coordination among agents exploits a Linda infrastructure. The proposal is validated in a prototype implementation developed in SICStus Prolog. To appear in Theory and Practice of Logic Programming (TPLP). Research partially funded by GNCS-INdAM projects, MUR-PRIN: Innovative and multidisciplinary approaches for constraint and preference reasoning project; NSF grants IIS-0812267 and HRD-0420407; and grants 2009.010.0336 and 2010.011.0403.  Multi-agent systems (MAS) can take many forms depending on the characteristics of the agents populating them. Amongst the more demanding properties with respect to the design and implementation of multi-agent system is how these agents may individually reason and communicate about their knowledge and beliefs, with a view to cooperation and collaboration. In this paper, we present a deductive reasoning multi-agent platform using an extension of answer set programming (ASP). We show that it is capable of dealing with the specification and implementation of the system's architecture, communication and the individual agent's reasoning capacities. Agents are represented as Ordered Choice Logic Programs (OCLP) as a way of modelling their knowledge and reasoning capacities, with communication between the agents regulated by uni-directional channels transporting information based on their answer sets. In the implementation of our system we combine the extensibility of the JADE framework with the flexibility of the OCT front-end to the Smodels answer set solver. The power of this approach is demonstrated by a multi-agent system reasoning about equilibria of extensive games with perfect information. A multiple contact filter connector capable of accommodating high RF currents and a method of manufacturing the same are disclosed. The connector includes an outer met allic shell, a dielectric body within the shell and at least one network filter contact assembly. The inner body has at least one through channel and a transverse cavity which communicates with the channel and an annular met allic ring disposed inwardly of the shell. The network filter contact assembly has a ground electrode and a pin electrode and is disposed within the portion of the channel bridging the cavity. Conductive curable filler material is charged into the cavity around and in contact with the ground electrode and annular ring to form a ground plate for the connector. A pair of spaced apart conductive plates may be disposed transversely to the ground electrode and ring to be in electrical contact therewith and the filler material. 