The Impact of Heterogeneity on Cooperating Agents (original) (raw)

Interoperable Intelligent Agents in a Dynamic Environment

New Advances in Intelligent Decision …, 2009

Current research conducted at the Knowledge-Based Intelligent Information and Engineering Systems (KES) Centre aims to improve/develop the communication aspects of an agent-oriented architecture that enables agents to automatically adapt their functionality at runtime based on message flows. Rigid designtime constraints can be replaced by a flexible plug-and-play componentized capability. Intelligent Agents (IAs) must possess interoperability and capability to share knowledge and context in order to achieve their goal(s). A concept demonstrator is being developed, using a number of dynamic distributed environments, to show how interoperable Multi-Agent Systems (MASs) can improve data flow in a distributed environment. The agents in this MAS are equipped with a number of sensors that provide data from the environment, which is fused to produce knowledge. The fused information is fed into an inference engine which contains the Subject Mater Expert (SME) knowledge-based required to make decision(s) and/or change some course of action.

From interoperability to cooperation: Building intelligent agents on middleware

Lecture Notes in Computer Science, 1998

As agent technologies are increasingly being involved in telecommunication-related applications, the need for open standards is becoming critical. During the past years, different scientific communities gave birth to different standardization actions, such as the Foundation for Physical Intelligent Agents (FIPA) and the Object Management Group's MASIF (Mobile Agent System Interoperability Facilities). Although they finally share some major targets, the OMG and FIPA current results show their distinct origins, respectively with a Distributed Artificial Intelligence and Multi-Agent Systems awareness on the one hand, and a telecommunication and information technologies background on the other hand. In a context where these two actions think about joining their achievements to upgrade each other, this article reports several experiments, carried out during the five past years in the agent platforms field, mixing both the intelligence and the middleware aspects. 1.2 Multi-Agent Platforms Many multi-agent platforms offer modelling and implementation solutions to the distribution of intelligence. To a certain extent, one may consider multi-expert systems and Distributed Artificial Intelligence as parts of the origins of the multi-agent systems. Following this point of view, we can see through the introduction, in typical centralized Artificial Intelligence languages, of low-level communication features (e.g. TCP/IP sockets in Lisp or Prolog), or more elaborate communication structures (e.g. blackboards and Linda Interactor in [20]), the emergence of the first multi-agent platforms. Then, sophisticated models (actors [1]) and techniques (constraints, reflexivity [10]) have been merged to enhance multi-agent platforms. But, as the enhancements are going on, the resulting diversity and heterogeneity makes it difficult for a standard to emerge, besides the AI classics, which industry is just beginning to appropriate to itself. Moreover, when it comes to distribution and communication, specific solutions are often applied, sometimes through simulation. But, the more these platforms integrate to standards, in real applications, the more striking is the proof of their accuracy. Then, it seems useful to find a way of defining a standards-based bridge between the real applications and multi-agent platforms, without preventing their evolutions, but enforcing reusability and interoperability.

Multiagent systems: Milestones and new horizons

Trends in Cognitive Sciences, 1997

Research i n m ultiagent systems (MAS), or Distributed AI, dates back to late 70's. Initial work in the area focused on distributed interpretation of sensor data, organizational structuring, and generic negotiation protocols. But several recent developments have helped reshape the focus of the eld. Like the rest of AI, the eld has matured from being largely exploratory in nature to focusing on formal theories of negotiation, distributed reasoning, multiagent learning, and communication languages. The eld is also maturing to the point o f d e v eloping its rst few elded applications. The recent widespread interest in the internet, the world-wide-web, and intelligent a g e n t applications have further fueled the need for techniques and mechanisms by which agents representing users can e ectively interact with other agents in open, dynamic environments. The development of several new international workshops and conferences have helped focus research in the area. The eld is poised at a critical juncture with stimulating problems and challenges promising some very exciting developments in the next few years.

Heterogeneous Distributed Cooperative Problem Solving System Helios and Its Cooperation Mechanisms

International Journal of Cooperative Information Systems, 1995

For advanced and complicated knowledge processing, we need to integrate various kinds of problem-solvers such as constraint solvers, databases, and application programs. A heterogeneous distributed cooperative problem solving system HELIOS achieves this integration by introducing capsule and environment modules. To integrate heterogeneous problem-solvers that may be implemented in different languages and may have different knowledge representations, those heterogeneity should be absorbed. Capsules and environments are introduced into HELIOS for this purpose. A capsule surrounds each problem-solver and translates the contents of communication to and from the internal representation and a common representation. We call an encapsulated problem-solver an agent. An environment is a module which provides a field giving common representation, and agents communicate and cooperate with each other in each environment. Since an encapsulated environment with its agents can be considered as an a...

A language and protocol to support intelligent agent interoperability

1992

We describe a language and protocol intended to support interoperability among intelligent agents in a distributed application. Examples of applications envisioned include intelligent multi-agent design systems as well as intelligent planning, scheduling and replanning agents supporting distributed transportation planning and scheduling applications. The language, KQML for Knowledge Query and Manipulation Language, is part of a larger DARPA-sponsored Knowledge Sharing Initiative focused on developing techniques and tools to promote the sharing on knowledge in intelligent systems. We will de ne the concepts which underlie KQML and attempt to specify its scope and provide a model for how it will be used.

Describing and Configuring Multi-Agent Systems at the Knowledge Level

Artificial intelligence research and …, 2003

Cooperative Problem Solving is usually focused on the coordination and cooperation mechanisms of Multi-Agent Systems, leaving out the user and the problem requirements. This paper introduces the knowledge description level of ORCAS (Open, Reusable and Configurable multi-Agent Systems), a framework to develop MAS applications configurable on demand, according to problem requirements and user preferences. ORCAS introduces the idea of configuring a MAS application at two layers, the knowledge and the operational layers. During the knowledge layer a configuration of MAS components is found, including agent capabilities and domain knowledge; and after that the knowledge configuration is operationalized by a customized team of problem solving agents. This framework is based on applying a knowledge modelling approach to describe Multi-Agent Systems, specifically, it proposes to describe agent capabilities as Problem-Solving Methods.

Trends in cooperative distributed problem solving

IEEE Transactions on Knowledge and Data Engineering, 1989

From this description of CDPS, we might ask: If coordination among problem solvers is di cult, why not build a single, more powerful problem solver to perform the functions of a CDPS network? In short, why C D P S ?

Communicating agents: an emerging approach for distributed heterogeneous systems

New Zealand Journal of Computing, 1995

The Department of Information Science is one of six departments that make up the Division of Commerce at the University of Otago. The department offers courses of study leading to a major in Information Science within the BCom, BA and BSc degrees. In addition to undergraduate teaching, the department is also strongly involved in postgraduate research programmes leading to MCom, MA, MSc and PhD degrees. Research projects in software engineering and software development, information engineering and database, software metrics, knowledge-based systems, natural language processing, spatial information systems, and information systems security are particularly well supported.