We present an extension to classical planning techniques that facilitates their use in complex multi-agent domains. The approach implements a form of metaplanning that enables a planner to reason about properties of multiple plans in a single plan network. With this approach, a planning agent can simultaneously generate an individual plan, repair a second, and, together with a group, execute a ...

Nowadays a multitude of different agent platforms exist that aim to support the software engineer in developing multi-agent systems. Nevertheless, most of these platforms concentrate on specific objectives and therefore cannot address all important aspects of agent technology equally well. A broad distinction in this field can be made between middlewareand reasoning-oriented systems. The first ...

The ability of recognizing a multi-agent plan a plan which has been generated for multiple executing agents implies the skill of recognizing the plan underlying a collective activity in which a group of agents is involved. Multi-agent plan recognition is a necessary and useful extension of current PR systems in some domains. In this work starting from CHAPLIN (CHart Applied to PLan INference, a...

Learning Automata (LA) were recently shown to be valuable tools for designing Multi-Agent Reinforcement Learning algorithms. One of the principal contributions of LA theory is that a set of decentralized, independent learning automata is able to control a finite Markov Chain with unknown transition probabilities and rewards. We extend this result to the framework of Multi-Agent MDP’s, a straigh...

This paper describes an approach to use embedded descriptions of agent actions in task ontologies and agent communication messages compliant to FIPA ACL [4, 5]. Ontological terms denoting actions are extended with semantical descriptive decorations in such a way that an agent requested to perform a well defined action is able to extract the formal description of the activity bound to this actio...

The current object-oriented development practice system analysis is documented through UML artifacts such as Class and Sequence diagrams. Since UML is a widely accepted modeling language, it also would be desirable to offer a UML support for the representation of agent-based system analysis artifacts. Although some central UML constructs are suitably matched for agent-based modeling, several im...

In this review, we present an analysis of the most used multi-agent reinforcement learning algorithms. Starting with single-agent algorithms, focus on critical issues that must be taken into account in their extension to scenarios. The analyzed algorithms were grouped according features. We a detailed taxonomy main approaches proposed literature, focusing related mathematical models. For each a...

agent oriented software engineering (aose) is an emerging field in computer science  and  proposes some systematic ideas for multi agent systems analysis, implementation and maintenance. despite the various methodologies introduced in the agent-oriented software engineering, the main challenges are defects in different aspects of methodologies. according to the defects resulted from weaknesses ...

We present a general view of what a \secure agent society" should be and how to develop it rather than focus on any speci c details or particular agent-based application. We believe that the main e ort to achieve security in agent societies consists of the following three aspects:1) agent authentication mechanisms that form the secure society's foundation, 2) a security architecture design with...

In this paper we introduce multi-agent causal models (MACMs) which are an extension of causal Bayesian networks to a multi-agent setting. Instead of 1 single agent modeling the entire domain, there are several agents each modeling non-disjoint subsets of the domain. Every agent has a causal model, determined by an acyclic causal diagram and a joint probability distribution over its observed var...