Catálogo de publicaciones - libros

The aim of this paper is to illustrate and sensitize reader to the variety of perspectives and the fundamental nature of organizations as stable/stabilizing systems and as multi-perspective action systems. Researchers have been explicitly thinking about MAS/DAI organizations and attempting to link formal (human) organization theory with MAS/DAI models for at least twenty years. Despite this, the idea of organizations has been a peripheral theme in MAS/DAI research---primarily a specific coordination technique---not really one of the central intellectual issues of the field. The theory of ‘natural’ organizations has a somewhat longer, more diverse, and more thorough intellectual history than that of organizations in MAS. Beyond recent work in human social and organization theory, some newer research on abstract organizations has been attempting to unify concepts in biology, chemistry, physics, mathematics, and computing theory (e.g., the lambda calculus), with those of natural social organizations and multi-agent systems. The landscape for thinking about organizations in MAS is growing quite interesting, and this paper surveys this landscape. It presents and contrasts some conceptions of organization that have emerged and proven useful, and attempts to show how these have been implemented, experimented with, and applied. It also projects some future directions for research on MAS organizations, and gives some thoughts on where the most exciting issues lie.

This chapter has two parts. In Part I, we present an overview of issues in modeling Multi Agent Systems (MAS), discuss what features and components are required for a MAS infrastructure, and present a model of a generic infrastructure. In addition, we present RETSINA as an example of an implemented MAS infrastructure. In Part II, we present issues in agent and service discovery and interoperation through a set of domain independent active and intelligent registries, called middle agents.

Logics for agents are useful when specifying, implementing and verifying agent programs.W e show that modal logic provides a nice tool to define informational, motivational and dynamic aspects of agents. We conclude by showing how an agent programming language can also benefit from this modal approach.

An Agent Communication Language (ACL) is a collection of speech-act-like message types, with agreed-upon semantics, which facilitate the knowledge and information exchange between software agents. From Knowledge Query and Manipulation Language (KQML) to FIPA ACL, ACL’s have been a cornerstone for the development of systems of communicating agents, and simultaneously they have been the subject of intensive standardization efforts.

Standardization’s goal is usability. As a result, although the initial focus on ACL’s revolved around establishing the semantics of ACL’s, a variety of usability-related questions have entered the picture of standardizing communication among agents. In this article, we present these questions and the work that addresses them, alongside the historical evolution of ACL’s, their semantics and the results of their standardization.

A prolific number of different Multi-Agent Systems (MAS) and associated applications have been developed in numerous research institutes and industrial laboratories world-wide. Perhaps the most important barrier to MAS making a successful transition from this research environment towards widespread adoption for consumer products and businesses, is the lack of interoperability between heterogeneous MA Systems. In 1996, the oundation for ntelligent hysical gents () was formed to provide a forum for developing specifications for agent systems. Since its formation, has increasingly focussed more on standardizing (multi-agent system) agent interoperability. As a result, it is often said that really stands for the oundation for nteroPerable gents. In this article, we discuss both technical and scientific issues in defining standards for interoperability between agents in different MA systems with a particular focus on the agent interoperability standards.

Distributed problem solving involves the collective effort of multiple problems solvers to combine their knowledge, information, and capabilities so as to develop solutions to problems that each could not have solved as well (if at all) alone. The challenge in distributed problem solving is thus in marshalling the distributed capabilities in the right ways so that the problem solving activities of each agent complement the activities of the others, so as to lead efficiently to effective solutions. Thus, while working together leads to distributed problem solving, there is also the distributed problem of how to work together that must be solved. We consider that problem to be a distributed planning problem, where each agent must formulate plans for what it will do that take into account (sufficiently well) the plans of other agents. In this paper, we characterize the variations of distributed problem solving and distributed planning, and summarize some of the basic techniques that have been developed to date.

This paper presents some of the key techniques for reaching agreements in multi-agent environments. It discusses game-theory and economics based techniques: strategic negotiation, auctions, coalition formation, market-oriented programming and contracting. It also presents logical based mechanisms for argumentations. The focus of the survey is on negotiation of self-interested agents, but several mechanisms for cooperative agents who need to resolve conflicts that arise from conflicting beliefs about different aspects of their environment are also mentioned. For space reasons, we couldn#x2019;t cover all the relevant works, and the papers that are mentioned only demonstrate the possible approaches. We present some of the properties of the approaches using our own previous work.

Agent-based systems suitable for dealing with applications where the environment is both dynamic and populated with competitors demand for sophisticated characteristics including adaptation, negotiation and coordination. We here briefly summarize some proposals on agents#x2019; negotiation capabilities including adaptation through reinforcement learning as well as qualitative multi-criteria negotiation and coalition formation protocols. Also, and inspired by robosoccer domain, some basic hints on knowledge representation for agents#x2019; team work are here described. All those proposals on automatic negotiation have been implemented through agent-based systems for different application domains (MACIV, SMACE, ForEV).

Agent integration architectures enable a heterogeneous, distributed set of agents to work together to address problems of greater complexity than those addressed by the individual agents themselves. Unfortunately, integrating software agents and humans to perform real-world tasks in a large-scale system remains difficult, especially due to two key challenges: ensuring robust execution in the face of a dynamic environment and providing abstract task specifications without all the low-level coordination details. To address these challenges, our Teamcore project provides the integration architecture with general-purpose teamwork coordination capabilities. We make each agent by providing it with a proxy capable of general teamwork reasoning. Thus, a key novelty and strength of our framework is that powerful teamwork capabilities are built into its foundations by providing the proxies themselves with a teamwork model called STEAM. While STEAM has earlier been demonstrated in domains involving homogeneous agent teams, its use in Teamcore proxies illustrates that teamwork models may also be applied in domains involving heterogeneous agents. Given STEAM, the Teamcore proxies addresses the first agent integration challenge, robust execution, by automatically generating the required coordination actions for the agents they represent. We can also exploit the proxies#x2019; reusable general teamwork knowledge to address the second agent integration challenge. Through , a developer specifies a hierarchical organization and its goals and plans, abstracting away from coordination details. Our integration architecture enables teamwork among agents with no coordination capabilities, and it establishes and automates consistent teamwork among agents with some coordination capabilities. We illustrate how the Teamcore architecture successfully addressed the challenges of agent integration in two application domains: simulated rehearsal of a military evacuation mission and facilitation of human collaboration.

The paper addresses the problems of efficient representation, maintenance and exploration of social knowledge enabling task decomposition, organization of negotiations, responsibility delegation and other ways of agents’ social reasoning. We focus on multi-agent systems for integration of already existing software components. It is supposed that all the social knowledge is kept separated from both the problem solving knowledge and agents#x2019; specific internal intelligence and that it is organized and administered in the acquaintance models located in the agents’ wrappers. A specific tri-base acquaintance model (3bA) is formalized and discussed throughout the paper. This model helps to optimize the communication traffic, to implement meta-reasoning processes and supports the machine learning activities. Several practical applications of the 3bA acquaintance model in different fields are presented and the acquired experience is discussed.