It is important, in situations where teams of agents can commito joint, long-term objectives, that the agents be able to identify whenthe teamobjective is no longer important, or is futile. Prior workhas typically ass...
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In this thesis, we introduce the distributed Learning Classifier System (DLCS) as anovel extension of J. H. Holland’s standard learning classifier system. While the standardLCS offers effective real-time control and ...
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In this thesis, we introduce the distributed Learning Classifier System (DLCS) as a
novel extension of J. H. Holland’s standard learning classifier system. While the standard
LCS offers effective real-time control and learning, one of its limitations is that it does not
provide a mechanism for allowing communication between LCS agents in a multiple-agent
scenario. Often multiple-agents are used to solve large tasks collectively by subdividing
the task into smaller parts. Multiple agents can also be used to solve a task in parallel so
that a solution can be arrived at more rapidly. With the DLCS, we introduce mechanisms
that satisfy both of these cases, while still providing compatible operation with the LCS.
We introduce three types of messages that can be passed between DLCS agents.
The first, the classifier message, allows agents to share learned information with one
another, thereby helping agents benefit from each other’s successes. The second, the
action message, allows agents to "talk" to one another. The third, the bucket brigade
algorithm payoff message, extends the chain rewarding payoff scheme of the standard
LCS to multiple DLCS agents.
Finally, we present some simulation results for both the standard LCS and the
DLCS. Our LCS simulations examine some of the important aspects of learning classifier
system operation, as well as illustrate some of the shortcomings. The DCLS simulations
justify the distributed architecture and suggest future directions for achieving learning
among multiple agents.
An evolving fitness based foraging and exploration strategy for a robot society formed by cooperative autonomous mobile robots in a spatially and temporally dynamic environment is presented. The operation of multiple ...
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An evolving fitness based foraging and exploration strategy for a robot society formed by cooperative autonomous mobile robots in a spatially and temporally dynamic environment is presented. The operation of multiple behaviour-based robots is controlled by an outside operator through modifications in members’ fitness functions. With a certain type of function formulation a collective society behavior will emerge and improve the society’s operation. The suggested concept is verified with simulations and some near future physical realizations are also presented.
In the framework of the Prometheus program and the sequel of this program, we are particularly interested by the development of an intelligent co-pilot in order to assist the driver. The development of such driver ass...
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In the framework of the Prometheus program and the sequel of this program, we are particularly interested by the development of an intelligent co-pilot in order to assist the driver. The development of such driver assistant is based on studies on car driving especially in cognitive psychology in order to take into account the characteristics of the different drivers. This kind of support systems is designed to help the driver in situations where he has some difficulties and is generally embedded in a larger architecture which includes real-time machine vision capability. It use sensors information about driver (behavior and intentions) and about vehicle and environment states. In this paper, we particularly focus on the design of the driver assistant system, its decision capabilities and its Human-Machine Interface. In order to validate our work, a demonstrator has been embedded on a Peugeot 605 and tested on real driving contexts.
This paper proposes to address the problem of work organization design in human-machine systems with the help of multiagent systems techniques. The organization, defined as the structure supporting tasks attribution, ...
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This paper proposes to address the problem of work organization design in human-machine systems with the help of multiagent systems techniques. The organization, defined as the structure supporting tasks attribution, coordination and communication, is the subject of many studies in distributed artificial intelligence (DAI). Three organization modes are presented. These modes are seen as foundations on which a global organization including humans and intelligent machines can be built, with some restrictions due to the characteristics of humans. Althought its simplifying aspect, this could be a step toward the application of multiagent concepts to address some of the humans-machines cooperation problems.
distributed artificial intelligence (DAI) is a subfield of artificialintelligence that deals with interactions of intelligent agents. Precisely, DAI attempts to construct intelligent agents that make decisions that a...
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distributed artificial intelligence (DAI) is a subfield of artificialintelligence that deals with interactions of intelligent agents. Precisely, DAI attempts to construct intelligent agents that make decisions that allow them to achieve their goals in a world populated by other intelligent agents with their own goals. This paper discusses major concepts used in DAI today. To do this, a taxonomy of DAI is presented. based on the social abilities of an individual agent. the organization of agents, and the dynamics of this organization through time. Social abilities are characterized by the reasoning about other agents and the assessment of a distributed situation. Organization depends on the degree of cooperation and on the paradigm of communication. Finally. the dynamics of organization is characterized by the global coherence of the group and the coordination between agents. A reasonably representative review of recent work done in DAI field is also supplied in order to provide a better appreciation of this vibrant AI field. The paper concludes with important issues in which further research in DAI is needed.
This paper introduces a new architecture for a real-time distributed artificial intelligence system: DENIS-a Dynamic Embedded Noticeboard Information System. The fundamental idea underlying the architecture draws heav...
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This paper introduces a new architecture for a real-time distributed artificial intelligence system: DENIS-a Dynamic Embedded Noticeboard Information System. The fundamental idea underlying the architecture draws heavily upon a distributed human system analogy, as seen, for example, in the workplace. The aim of DENIS is to provide a simple, meaningful means by which autonomous intelligent agents can cooperate and coordinate their actions in order to enhance the reliability and effectiveness of a real-time distributed control system. Based on a human paradigm, the architecture inherently allows for the control of an intelligent agent to be taken over by a human operator, yet still to maintain consistency in the distributed system. The key to the thinking in this new approach is to try to model how humans work together, and to implement this in a distributed architecture. One of the main issues raised is that humans owe much of their flexibility to their ability to not only logically, but also in terms of time.
Increased demand by the public for diverse and quality recreation opportunities has placed considerable pressure on the natural resource and its management. This problem is compounded by a general lack of understandin...
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Increased demand by the public for diverse and quality recreation opportunities has placed considerable pressure on the natural resource and its management. This problem is compounded by a general lack of understanding of interactions between people and forest recreation environments that result in wide variations in perceptions, expectations, and patterns of choice and use. Emerging technologies, such as distributed artificial intelligence, provide a mechanism to integrate advances in recreation research with a Geographic Information Systems (GIS) based environment. distributed artificial intelligence provides the foundation for a modeling system to simulate the interactions between recreators and their environment. Despite the work done by many researchers in the development of object-oriented modeling and simulation languages, GIS, nonhuman agent design and simulations, no single system has been constructed to handle the complexity of goal-oriented autonomous human agents seeking recreational opportunities in natural environments. This paper describes a theoretical framework and a model for simulating hiker behavior in a natural environment using intelligent agents, discrete event simulation (DEVS) and GIS data. The results of hiker interactions are summarized to provide feedback on the implications for alternative recreation management planning.
In recent years, there has been growing use of distributed systems, and therefore a growing demand for tools able to manage them. However, these tools are not always well suited to their task, in part because of the l...
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In recent years, there has been growing use of distributed systems, and therefore a growing demand for tools able to manage them. However, these tools are not always well suited to their task, in part because of the lack of built-in intelligence. This paper discusses how to introduce intelligence to distributed systems management and control. To do so we analyse requirements for distributed systems management and focus on management tasks from the architecture and model points of view. intelligence is introduced through distributed artificial intelligence concepts and multiagent systems. We illustrate the application of our techniques with an example based on traffic and congestion control in ATM networks.
The paper presents a knowledge and belief representation system for multiple agents called MAKRS. MAKRS can represent and reason about multi-agent knowledge and belief as well as factual knowledge of the actual world....
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The paper presents a knowledge and belief representation system for multiple agents called MAKRS. MAKRS can represent and reason about multi-agent knowledge and belief as well as factual knowledge of the actual world. It can also process some axioms which are usually used to represent the characteristics of each agent's knowledge and belief. MAKRS can be used as a tool to construct knowledge bases for various applications where multiple agents co-exist, and some sample knowledge bases are constructed to demonstrate its usefulness and practicality.
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