John Phelps

An open agent architecture for assisting elder independence

We are building an agent-oriented system to aid elderly people to live longer in their homes, increasing the duration of their independence from round-the-clock care while maintaining important social connectedness and reducing caregiver burden. The Independent LifeStyle Assistant

TÆMS agents: enabling dynamic distributed supply chain management

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A key-based coordination algorithm for dynamic readiness and repair service coordination

(I.L.S.A.) is a multiagent system that incorporates a unified sensing model, probabilistically derived situation awareness, hierarchical task network response planning, real-time action selection control, complex coordination, and machine learning. This paper describes the problem, our reasoning for selecting an agent-based approach, and the architecture of the system.

COORDINATORS: Coordination Managers for First Responders

Abstract Some dynamic supply chain problems are instances of a class of distributed optimization problems that TAEMS and other intelligent agents were made to address. In this paper we define a discrete distributed dynamic supply chain management problem and specify how TAEMS agents, equipped with new coordination mechanisms, automate and manage the supply chain. The agents increase the level of flexibility in the chain and enable members of the supply chain to be more responsive through producer/consumer negotiation and reasoning about manufacturing availability, raw material requirements, and shipping time requirements. Planning/scheduling and coordination research enables the agents to perform this level of automation on-line , responding to change as it happens in the environment, rather than relying on precomputed solutions or reasoning via abstract flow characterizations.

Key-Based Coordination Strategies: Scalability Issues

This paper describes an agent application for the coordination of air-craft repair, refit, refuel, and rearm teams in a dynamic setting. The paper also presents a new algorithm for dynamic distributed service team coordination and compares its performance to an optimal cen-tralized service team scheduler.

Centralized VS. Decentralized Coordination: Two Application Case Studies

COORDINATORs are coordination managers for fielded first responders. Each first response team is paired with a COORDINATOR coordination manager which is running on a mobile computing device. COORDINATORs provide decision support to first response teams by reasoning about who should be doing what, when, with what resource, in support of which other team, and so forth. COORDINATORs respond to the dynamics of the environment by (re)coordinating to determine the right course of action for the current circumstances. COORDINATORs have been implemented using wireless PDAs and proprietary first responder location tracking technologies. This paper describes COORDINATORs, the motivation for them, the underlying agent architecture, evaluation first response exercises, research issues, and next steps for more advanced cognitive COORDINATORs that learn and perform more sophisticated operations.

System and method for automated monitoring, recognizing, supporting, and responding to the behavior of an actor

We describe a key-based approach to multi-agent coordination, where certain coordination decisions are done only when the agent holds a coordination key. This approach is primarily decentralized, but has some centralized aspects, including synchronization of coordination decisions and schedule information sharing. The approach is described within the context of the application requirements that motivated its development. Finally, its scalability properties are discussed.

The independent lifestyle assistant™ (I.L.S.A.): AI lessons learned

This paper examines two approaches to multi-agent coordination. One approach is primarily decentralized, but has some centralized aspects, the other is primarily centralized, but has some decentralized aspects. The approaches are described within the context of the applications that motivated them and are compared and contrasted in terms of application coordination requirements and other development constraints.