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Transcript of 1 Multiagent Teamwork: Analyzing the Optimality and Complexity of Key Theories and Models David V....
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Multiagent Teamwork:Analyzing the Optimality and Complexity of Key Theories and Models
David V. Pynadath and Milind Tambe
Information Sciences Institute and
Department of Computer Science
University of Southern California
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Agent Teamwork
Agents, robots, sensors, spacecraft, etc. Performing a common task Operating in an uncertain environment Distinct, uncertain observations Distinct actions with uncertain effects Limited, costly communication
Battlefield Simulation Satellite Clusters Disaster Rescue
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MotivationP
erfo
rman
ce
Complexity
OptimalNew algorithm
TheoreticalApproaches
No communication
Practical Systems
?
?
?
?Optimal
Outline of Results1) Unified teamwork framework
2) Complexity of optimal teamwork
3) New coordination algorithm
4) Optimality-Complexity evaluation of existing methods
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Enemy Radar
Example Domain:Helicopter Team
Goal
Did theysee that? I destroyed the
enemy radar.
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Communicative Multiagent Team Decision Problem (COM-MTDP)
S: states of the world e.g., position of helicopters, position of the enemy
A: domain-level actions e.g., fly below radar, fly normal altitude
P: transition probability function e.g., world dynamics, effects of actions
: communication capabilities, possible “speech acts” e.g., “I have destroyed enemy radar.”
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COM-MTDPs (cont’d)
: observations e.g., enemy radar, position of other helicopter
O: probability (for each agent) of observation Maps state and actions into distribution over
observations (e.g., sensor noise model)R: reward (over states, actions, messages) e.g., good if we reach destination, better if we reach it earlier
e.g., saying, “I have destroyed enemy,” has a cost
Teamwork Definition: All members share same preferences (i.e., R)
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Problem Complexity
COM-MTDPs
Free communication
Collectively Observable
Individually Observable
No communication
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To Communicate orNot To Communicate
Local decision of one agent at a single point in time: “I have achieved a joint goal.” “Should I tell my teammate?”
Joint intentions theory: “I must attain mutual belief.” Always communicate [Jennings]
STEAM: “I must communicate if the expected cost of
miscoordination outweighs the cost of communication.” [Tambe]
Each cost is a fixed parameter specified by designer
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Communicate if and only if: E[R | communicate] E[R | do not communicate]
Locally Optimal Criterionfor Communication
Expectation over possible histories of states and beliefs up to current time
Expected reward over future trajectories of states and beliefs WITH communication
Expected reward over future trajectories of states and beliefs WITHOUT communication
Expected cost of communicating
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Empirical Results
Communication Cost
Observability
V_opt-V
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Empirical Results
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Empirical Results
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Silent
Optimality vs. Complexity
Op
tim
alit
y
Complexity
Globally OptimalLocally Optimal
STEAM
Jennings
seconds(log)0.1 1.0 10,000
E[R]
1.46
1.43
1.40 Observability = 0.2Comm. Cost = 0.7
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Jennings
Optimality vs. Complexity
Op
tim
alit
y
Complexity
Globally OptimalLocally Optimal
STEAM
Silent
seconds(log)0.1 1.0 10,000
E[R]
1.80
1.43 Observability = 0.2Comm. Cost = 0.3
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Summary
COM-MTDPs provide a unified framework for agent teamwork Representation subsumes many existing agent models Policy space subsumes many existing prescriptive theories
This framework supports deeper analyses of teamwork problems Quantitative characterization of optimality-efficiency
tradeoff , for different policies, in different domains Derivation of novel coordination algorithms
http://www.isi.edu/teamcore/Teamwork Detailed proofs Source code JAIR article