Valerie Guralnik

TÆMS agents: enabling dynamic distributed supply chain management

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.

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

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.

COORDINATORS: Coordination Managers for First Responders

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.

On handling dependent evidence and multiple faults in knowledge fusion for engine health management

Diagnostic architectures that fuse outputs from multiple algorithms are described as knowledge fusion or evidence aggregation. Knowledge fusion using a statistical framework such as Dempster-Shafer (D-S) has been used in the context of engine health management. Fundamental assumptions made by this approach include the notion of independent evidence and single fault. In most real world systems, these assumptions are rarely satisfied. Relaxing the single fault assumption in D-S based knowledge fusion involves working with a hyper-power set of the frame of discernment. Computational complexity limits the practical use of such extension. In this paper, we introduce the notion of mutually exclusive diagnostic subsets. In our approach, elements of the frame of discernment are subsets of faults that cannot be mistaken for each other, rather than failure modes. These subsets are derived using a systematic analysis of connectivity and causal relationship between various components within the system. Specifically, we employ a special form of reachability analysis to derive such subsets. The theory of D-S can be extended to handle dependent evidence for simple and separable belief functions. However, in the real world the conclusions of diagnostic algorithms might not take the form of simple or separable belief functions. In this paper, we present a formal definition of algorithm dependency based on three metrics: the underlying technique an algorithm is using, the sensors it is using, and the feature of the sensor that the algorithm is using. With this formal definition, we partition evidence into highly dependent, weakly dependent and independent evidence. We present examples from a Honeywell auxiliary power unit to illustrate our modified D-S method of evidence aggregation

Key-Based Coordination Strategies: Scalability Issues

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.

Centralized VS. Decentralized Coordination: Two Application Case Studies

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.

A stochastic theory for evidence aggregation

The problem of evidence aggregation arises when opinions are provided by multiple experts. Current evidence aggregation approaches view fusion as a one-shot problem, completely disregarding condition evolution over time. In this work, we propose a completely new theory for evidence aggregation, formulating the aggregation problem as an estimation/filtering problem. The aggregation problem is viewed as a partially-known Markov process. The overall belief is modeled as a known, but unobservable, state evolving in a linear state space. Diagnostic algorithms provide noisy observation for the hidden states of the belief space. We demonstrate the accuracy and variability of the proposed approach under conditions of sensor noise and diagnostic algorithm drop-out. Further, we provide empirical evidence of convergence and management of the combinatorial complexity associated with handling multiple fault hypotheses.