Coordination, the process by which an agent reasons about its local actions and the (anticipated) actions of others to try and ensure the community acts in a coherent manner, is perhaps the key problem of the discipline of Distributed Artificial Intelligence (DAI). In order to make advances

In this paper we make three main contributions (fully de-tailed in [5]). Firstly, we formulate a new algorithm, the so-called Action-GDL, which extends GDL [1] to apply it to Distributed Constraint Optimization Problems (DCOPs). Secondly, we show that Action-GDL generalizes DPOP[4], a low

for Distributed Artificial Intelligence (DAI), giving special attention to systems for manufacturing scheduling and control. It describes a taxonomy of such systems, gives case studies of several advanced research applications and actual industrial installations, and identifies steps that need to be taken

or increasing the knowledge of a single agent by implementing concept of Multi agent into it. Index Terms- Distributed artificial intelligence, learning, multi-agent, reasoning about others, single-agent.

. In distributed artificial intelligence systems it is important that the constituent intelligent systems communicate. This may be a problem if the systems use different methods to represent uncertain information. This paper presents a method that enables systems that use different uncertainty

Arti cial Intelligence through distributed problem solving. Agents are persistent and goal directed entities that may move between hosts in response to changes in requirements such as security and eciency, and that would normally be limited in the computational resources they may employ in pursuing

agents have only partial information, assumptions of greater interest for artificial intelligence. A novel, stable, negotiation protocol is introduced for the case of agents who are able to share a discrete set of tasks with one another. The case of agents who may lie to one another during

Distributed W-Learning (DWL) is a reinforcement learning-based algorithm for multi-policy optimization in agent-based systems. In this poster we propose the use of DWL for de-centralized multi-policy optimization in autonomic systems. Using DWL, agents learn and exploit the dependencies be

High reliability and availability are of utmost importance to the majority of today’s software-based systems. Typically, some kind of redundancy is used in order to achieve high reliability and availability in case of faults. Dynamic redundancy setups employ backup systems which take over as soon as the primary system fails. Additionally, central fault tolerance management systems are often used to guide the replication process. In this paper, we present an approach which relies on emergent fault-tolerance. The desired behavior is the result of local reconfigurations of self-organizing agents. This allows us to abandon the classical distinction of primary and backup systems as well as the need for a central management system. Our experimental results show the benefit of our approach. Categories and Subject Descriptors

This paper presents some aspects of our studies of the conceptual and implementation relationships between Object-Based Concurrent Programming (OBCP) and Distributed Artificial Intelligence (DAI). The basic argument of this paper is that to implement DAI systems we should find complementary