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

MokSAF: How should we support teamwork in human-agent teams?
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.
s.n.
Lenox, Terri L.
afaa3190-e05a-49e3-944c-f1d4f407f3c6
Payne, Terry R.
09574f42-3cbc-43d3-86c2-2f96eb6319a0
Hahn, Susan
565d8016-cdb8-43d7-89d9-390367ca0b64
Lewis, Michael
f046ffc3-6d03-4a51-9c3d-fa8fa6a01473
Sycara, Katia
df200c43-d34d-4093-bb4e-493fea2d0732
Lenox, Terri L.
afaa3190-e05a-49e3-944c-f1d4f407f3c6
Payne, Terry R.
09574f42-3cbc-43d3-86c2-2f96eb6319a0
Hahn, Susan
565d8016-cdb8-43d7-89d9-390367ca0b64
Lewis, Michael
f046ffc3-6d03-4a51-9c3d-fa8fa6a01473
Sycara, Katia
df200c43-d34d-4093-bb4e-493fea2d0732

Lenox, Terri L., Payne, Terry R., Hahn, Susan, Lewis, Michael and Sycara, Katia (1999) MokSAF: How should we support teamwork in human-agent teams? s.n.

Record type: Monograph (Project Report)

Abstract

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.

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Published date: 1999
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 263090
URI: http://eprints.soton.ac.uk/id/eprint/263090
PURE UUID: 6fe94855-421f-47be-9e71-e6dbbb5d941d

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Date deposited: 09 Oct 2006
Last modified: 23 Sep 2020 16:43

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