Agent-Based Team Aiding in a Time Critical Task

In this paper we evaluate the effectiveness of agent-based aiding in support of a time-critical team-planning task for teams of both humans and heterogeneous software agents. The team task consists of human subjects playing the role of military commanders and cooperatively planning to move their respective units to a common rendezvous point, given time and resource constraints. The objective of the experiment was to compare the effectiveness of agent-based aiding for individual and team tasks as opposed to the baseline condition of manual route planning. There were two experimental conditions: the Aided condition, where a Route Planning Agent (RPA) finds a least cost plan between the start and rendezvous points for a given composition of force units; and the Baseline condition, where the commanders determine initial routes manually, and receive basic feedback about the route. We demonstrate that the Aided condition provides significantly better assistance for individual route planning and team-based re-planning

MokSAF: How should we support teamwork in human-agent teams?

In this paper, we describe an interface agent, two different route planning agents and a pilot study which examined whether these agents could support a team planning task. The MokSAF interface agent links an Artificial Intelligence (AI) route-planning agent to a Geographic Information System (GIS). The user specifies a start and an end point and the route-planning agent finds a minimum cost path between the points. The user is allowed to define additional “intangible” constraints (not due to terrain characteristics) corresponding to geographic regions, which can be used to steer the agent’s behavior in a desired direction. A second agent (the naive route planning agent, or Naive RPA) has access to the same knowledge of the terrain and cost functions available to the Autonomous RPA, but uses this knowledge to critique paths specified by the user. We hypothesize that as the complexity of intangible aspects of a planning problem increase, the Naive RPA will improve in relative performance. The reported study found advantages across the board for the Autonomous RPA in a team-planning task

Agent-based aiding for individual and team planning tasks

Intelligent aiding strategies were evaluated for a team planning task. The MokSAF interface agent links an Artificial Intelligence (AI) route planning agent to a Geographic Information System (GIS). Through this agent, the user specifies a start and an end point, and describes the composition and characteristics of a military platoon. Two aided conditions and one non-aided condition were examined. In the first aided condition, a route-planning agent determines a minimum cost path between the specified end points. The user is allowed to define additional “intangible” constraints that describe situational or social information. In the second aided condition the same knowledge of the terrain is used to plot the best route within bounds specified by the user. In the control condition the user draws a route without help. The reported study found across the board advantages for agent-based aiding when routes were merged in a team-planning task

Varying the User Interaction within Multi-Agent Systems

Agents within an open multi-agent system are located through their advertisements with middle agents. Such advertisements describe the agent's capability, ontology, query or task specification, and details about the data returned once the task has been completed. Agents may have similar capabilities, but exhibit different models of user interaction. A case study of a multi-agent system is described which contains a variety of different agents, some of which have functionally similar capabilities but involve different types of user interaction. We demonstrate how the choice of user interaction can have a significant effect on the performance of the whole agent community. This leads to the proposal that an agent's interactive style should also be included within its capability advertisement

Yildiz OG, Soyuer S, Saraymen R, Eroglu C.Protective effects of caffeic acid phenethyl ester on radiation induced lung injury in rats.Clin Invest Med. 2008 Oct 1;31(5):E242-

In this paper, we present an interface agent, MokSAF, which facilitates time-critical team-planning tasks for teams of both humans and heterogeneous software agents. This agent assists in the formation of teams of humans (via other MokSAF agents) and task agents that can autonomously perform team subtasks. It provides a suitable interaction mechanism to instruct the various task agents in the team; and, by monitoring the human's progress, reallocate or modify the sub-tasks if the human fails to achieve that subtask. A military domain has been used to investigate this interface agent. The task consists of three military (human) commanders that each assemble a platoon, and plan routes so that all three platoons arrive at a given rendezvous by a specified time. An experimental study has been conducted to evaluate MokSAF and the assistance provided by one of three different task agents, and the results summarized. Keywords Interface Agents, Functional Substitutability, Multi-Agent System..