Agent architectures need to organize themselves and adapt dynamically to changing circumstances without top-down control from a system operator. Some researchers provide this capability with complex agents that emulate human intelligence and reason explicitly about their coordination, reintroducing many of the problems of complex system design and implementation that motivated increasing software localization in the first place. Naturally occurring systems of simple agents (such as populations of insects or other animals) suggest that this retreat is not necessary. This paper summarizes several studies of such systems, and derives from them a set of general principles that artificial multi-agent systems can use to support overall system behavior significantly more complex than the behavior of the individuals agents. 1.

Modern UAV’s reduce the threat to human operators, but do not decrease the manpower requirements. Each aircraft requires a flight crew of one to three, so deploying large numbers of UAV’s requires committing and coordinating many human warfighters. Insects perform impressive feats of coordination without direct inter-agent coordination, by sensing and depositing pheromones (chemical scent markers) in the environment [14]. We have developed a novel technology for coordinating the movements of multiple UAV’s based on a computational analog of pheromone dynamics. The control logic is simple enough that it can be executed autonomously by a UAV, enabling a single human to monitor an entire swarm of UAV’s. This paper describes the technology, its application to UAV coordination, and the results we have obtained.

Agents guided by synthetic pheromones can imitate the behavior of insects in tasks such as path planning. These systems are well suited to problems such as path planning for unmanned robotic vehicles. We have developed a model for controlling robotic vehicles in combat missions using synthetic pheromones. In the course of our experimentation, we have identified the need for proper tuning of the algorithms to get the desired behavior. We briefly describe the synthetic pheromone mechanisms for dynamically finding targets and planning safe paths. Genetic algorithms for automatically tuning the behavior of the pheromone equations are described.

... manufacturing, and logistics require that entities change location under certain constraints. These problems are traditionally addressed with centralized control mechanisms, which become bottlenecks and points of vulnerability. More recent multi-agent negotiation schemes enable the entities to maintain the desired constraints among themselves in decentralized fashion. This paper explores a particularly simple form of coordination that replaces central coordination and agent-to-agent communication with interaction through a shared environment. Inspired by pheromone mechanisms in natural insect populations, this method is capable of solving the classical Missionaries and Cannibals movement problem. We illustrate how agents can be programmed to interact using synthetic pheromones, and evaluate the performance of our algorithms for four different levels of pheromone information and two different approaches to constructing the functions by which agents respond to the pheromones.

The Center for Electronic Commerce (CEC) embodies over fourteen years of experience in applying agents to industrial problems. We have found such a fit in three areas: coordination of industrial designers, simulation and modeling of complex products and processes, and scheduling and control of production systems. This presentation outlines several trends in modern manufacturing, describes how these trends affect the three problem areas, discusses the features of agents that make them attractive candidates for implementing such systems, and reviews example applications from CEC's portfolio in each of these areas.

Neural networks have traditionally been applied to recognition problems, and most learning algorithms are tailored to those problems. We discuss the requirements of learning for generalization, where the traditional methods based on gradient descent have limited success. We present a new stochastic learning algorithm based on simulated annealing in weight space. We verify the convergence properties and feasibility of the algorithm. We also describe an implementation of the algorithm and validation experiments. 1. Introduction Neural networks are being applied to a wide variety of applications from speech generation[1], to handwriting recognition[2]. Last decade has seen great advances in design of neural networks for a class of problems called recognition problems, and in design of learning algorithms[3-5, 5-7]. The learning of weights for neural network for many recognition problem is no longer a difficult task. However, designing a neural network for generalization problem is ...

Computer agent based systems provide a natural mechanism to reflect real world human agent based systems. Supply chains are networks of corporations involving multiple human agents in connected but disparate processes. We investigate some ways that computer agent-based systems can assist and supplant human-based interaction and decision making in a supply chain without the need for unwieldy centralized or top-down management schemes. The Agent Network for Task Scheduling (ANTS) architecture uses techniques inspired by both human institutions and insect colonies. In ANTS large populations of simple agents exhibit robust behavior in scheduling supply chains. We describe a new mechanism called least commitment scheduling that defers decisions on process sequences until the last possible moment. A Densitybased Emergent Scheduling Kernel (DESK) uses probabilistic committed capacity profiles of resources over time to provide a surface over which the agents can wander looking for opportunities to optimize. 1.

Designs for real-world agent-based systems must reflect both domain requirements and technical capabilities. We illustrate some of the requirements for agents in manufacturing scheduling and an architecture that addresses them with a case study of AARIA (Autonomous Agents for Rock Island Arsenal), an industrial-strength agent-based shop-floor control and scheduling architecture being developed for an Army manufacturing facility. A review of other agent-based manufacturing systems illustrates how the design choices made in such systems reflect the requirements anticipated by the authors. Keywords Agents, Requirements, Life Cycle, System Design, Scheduling, Manufacturing, AARIA 1. Introduction Successful application of agents (as of any technology) must reconcile two perspectives. The researcher focuses on a particular agent capability (e.g., communication, planning, learning), and seeks practical problems to demonstrate the usefulness of this capability (and justify further funding)...

Agent architectures need to organize themselves and adapt dynamically to changing circumstances without top-down control from a system operator. Some researchers provide this capability with complex agents that emulate human intelligence and reason explicitly about their coordination, reintroducing many of the problems of complex system design and implementation that motivated increasing software localization in the first place. Naturally occurring systems of simple agents (such as populations of insects or other animals) suggest that this retreat is not necessary. This paper summarizes several studies of such systems, and derives from them a set of general principles that artificial multiagent systems can use to support overall system behavior significantly more complex than the behavior of the individuals agents.