TY - JOUR
T1 - Towards Flexible Teamwork in Persistent Teams
T2 - Extended Report
AU - Tambe, Milind
AU - Zhang, Weixiong
N1 - Funding Information:
The research was supported in part by NSF grant IRI-9711665, in part by NSF grant IRI-9619554, and in part by contract N66001-95-C-6013 from ARPA/ISO. We thank Randy Hill, Jon Gratch and Paul Rosenbloom for discussion of issues related to the ACTD demonstration, the RoboCup simulation group for discussion of issues related to RoboCup, and Keith Decker and Tom Wagner for discussions related to STEAM’s relationship with GPGP. We also thank the reviewers of ICMAS’98 and of this special issue of AAMAS—they helped us to signi cantly improve the quality of this article. This article extends our earlier conference paper [37] from ICMAS’98.
PY - 2000
Y1 - 2000
N2 - Teamwork is a critical capability in multi-agent environments. Many such environments mandate that the agents and agent-teams must be persistent i.e., exist over long periods of time. Agents in such persistent teams are bound together by their long-term common interests and goals. This paper focuses on flexible teamwork in such persistent teams. Unfortunately, while previous work has investigated flexible teamwork, persistent teams remain unexplored. For flexible teamwork, one promising approach that has emerged is model-based, i.e., providing agents with general models of teamwork that explicitly specify their commitments in teamwork. Such models enable agents to autonomously reason about coordination. Unfortunately, for persistent teams, such models may lead to coordination and communication actions that while locally optimal, are highly problematic for the team's long-term goals. We present a decision-theoretic technique based on Markov decision processes to enable persistent teams to over-come such limitations of the model-based approach. In particular, agents reason about expected team utilities of future team states that are projected to result from actions recommended by the teamwork model, as well as lower-cost (or higher-cost) variations on these actions. To accommodate real-time constraints, this reasoning is done in an any-time fashion. Implemented examples from an analytic search tree and some real-world domains are presented.
AB - Teamwork is a critical capability in multi-agent environments. Many such environments mandate that the agents and agent-teams must be persistent i.e., exist over long periods of time. Agents in such persistent teams are bound together by their long-term common interests and goals. This paper focuses on flexible teamwork in such persistent teams. Unfortunately, while previous work has investigated flexible teamwork, persistent teams remain unexplored. For flexible teamwork, one promising approach that has emerged is model-based, i.e., providing agents with general models of teamwork that explicitly specify their commitments in teamwork. Such models enable agents to autonomously reason about coordination. Unfortunately, for persistent teams, such models may lead to coordination and communication actions that while locally optimal, are highly problematic for the team's long-term goals. We present a decision-theoretic technique based on Markov decision processes to enable persistent teams to over-come such limitations of the model-based approach. In particular, agents reason about expected team utilities of future team states that are projected to result from actions recommended by the teamwork model, as well as lower-cost (or higher-cost) variations on these actions. To accommodate real-time constraints, this reasoning is done in an any-time fashion. Implemented examples from an analytic search tree and some real-world domains are presented.
KW - Markov decision processes
KW - Multi-agent systems
KW - Persistence
KW - Teamwork
UR - http://www.scopus.com/inward/record.url?scp=0034411115&partnerID=8YFLogxK
U2 - 10.1023/A:1010026728246
DO - 10.1023/A:1010026728246
M3 - Article
AN - SCOPUS:0034411115
SN - 1387-2532
VL - 3
SP - 159
EP - 183
JO - Autonomous Agents and Multi-Agent Systems
JF - Autonomous Agents and Multi-Agent Systems
IS - 2
ER -