Thomas Wagner

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.

Evolving Real-Time Local Agent Control for Large-Scale Multi-agent Systems

Control for agents situated in multi-agent systems is a complex problem. This is particularly true in hard, open, dynamic environments where resource, privacy, bandwidth, and computational limitations impose restrictions on the type of information that agents may share and the control problem solving options available to agents. The MQ or motivational quantities framework addresses these issues by evaluating candidate tasks based on the agents organizational context and by framing control as a local agent optimization problem that approximates the global problem through the use of state and preference.

Integrative negotiation in complex organizational agent systems

This paper addresses the problem of negotiation in a complex organizational context and tries to bridge the gap between self-interested negotiation and cooperative negotiation. An integrative negotiation mechanism is introduced, which enables agents to choose any attitude from the extremes of self-interested and cooperative to those that are partially self-interested and partially cooperative. This mechanism is based on and also extends the motivational qualities(MQ) framework for evaluating which task an agent should pursue at each time point. Experimental work verifies this mechanism and explores the question whether it always improves the social welfare to have an agent be completely cooperative.

A two-level negotiation framework for complex negotiations

In this paper, we present a negotiation framework in which the negotiation process is performed at two levels. The upper level deals with the formation of high-level goals and objectives for the agent, and the decision about whether or not to negotiate with other agents to achieve particular goals or bring about particular objectives. Negotiation at this upper level determines the rough scope of the commitment. The lower level deals with feasibility and implementation operations; negotiation at this level involves refinement of the rough commitments proposed at the upper level. The experimental work shows this two-level negotiation framework enables the agent to handle complicated negotiation issues and uncertainties in a more efficient way.

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 multi-leveled negotiation framework

In this paper, we present a multi-leveled negotiation framework in which the negotiation process is performed at two levels. The upper level deals with the formation of high level goals and objectives for the agent, and the decision about whether or not to negotiate with other agents to achieve particular goals or bring about particular objectives. The negotiation at this upper level determines the rough scope of the commitment (i.e. the time and the quality characteristics) and the cost of the commitment. The lower level deals with feasibility and implementation operations, such as the detailed analysis of candidate tasks and actions and the formation of the detailed temporal and resource-specific commitments among agents. The negotiation at this lower level involves the refinement of the rough commitments proposed at the upper level. The experimental work shows this two-leveled negotiation framework enables the agent to handle complicated negotiation issues and uncertainties in a more efficient way. 1. NEGOTIATION AT DIFFERENT LEVELS Usually negotiation is structured as a single level process from the proposal to the final commitment, all related issues such as finishing time, achieved quality and offered price are determined in this process. This negotiation can require a complicated reasoning process when the agent has multiple tasks where the tasks may be achieved in different ways and include a sequence of activities, some of which may require external or internal resources. Additionally, uncertainty in task execution may further complicate the negotiation process as behavior deviates from the expected. The This material is based upon work supported by the National Science Foundation under Grant No.IIS-9812755 and the Air Force Research Laboratory/IFTD and the Defense Advanced Research Projects Agency under Contract F30602-99-2-0525. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Defense Advanced Research Projects Agency, Air Force Research Laboratory/IFTD, National Science Foundation, or the U.S. Government. deviation can cause re-negotiation over commitments or the adjustment of local activities so as to still meet the commitments. This paper explores an alternative approach to negotiation in which the negotiation process is performed at different abstraction levels to reduce the complexity of the search. The upper level deals with the formation of high level goals and objectives for the agent, and the decision about whether or not to negotiate with other agents to achieve particular goals or bring about particular objectives. The negotiation at this upper level determines the rough scope of the commitment (i.e. the time and the quality characteristics) and the cost of the commitment. The lower level deals with feasibility and implementation operations, such as the detailed analysis of candidate tasks and actions and the formation of the detailed temporal and resource-specific commitments among agents. The negotiation at this lower level also involves the refinement of the rough commitments proposed at the upper level. Let’s look at an example to make these issues concrete. Agent is Adam’s personal assistant agent. Agent is deigned to carry out multiple tasks corresponding to Adam’s multiple goals in his life. Adam is a professor of Asian culture and language and he also has a family. He is asked by his department chair whether he can deliver a talk about his recent research results at the college. Also, he is planning to attend a conference in his research area. Meanwhile, his wife discusses with him the arrangement for their son’s birthday party. Thus, there are three candidate tasks that appear in the agenda of agent : prepare a talk for Adam’s lecture, plan Adam’s trip to a conference, and organize a birthday party for Adam’s son. These tasks are associated with Adam’s different roles, and contribute to different goals. The contributions of these tasks are not interchangeable. Each task has a deadline request, and also has multiple alternative ways to be performed. Figure 1 shows these three tasks. The higher level view describes the deadline for each task, the abstracted plans for each task, the duration of these plans and how they contribute to different goals. The lower level view describes the detailed plan for each task with the specification of the execution characteristics for each primitive tasks. Figure 2 presents the detailed plan for task prepare talk. Agent needs to make decisions about which tasks should be performed, and when and how to perform them. The possible issues that agent can negotiate about include: 1. Negotiation with the secretary agent about when the talk should be delivered, which affects the deadline of the task prepare talk. 2. Negotiation with a translator agent about the task translate material, which includes when this task can be performed and how much it costs. 3. Negotiation with a travel agent about the task book ticket, which includes when this task can be performed and how