@conference {1505636,
	title = {Teamwork in Distributed POMDPs: Execution-time Coordination Under Model Uncertainty },
	booktitle = {International Conference on Autonomous Agents and Multiagent Systems (Extended Abstract)	},
	year = {2011},
	abstract = {Despite their NEXP-complete policy generation complexity [1],
Distributed Partially Observable Markov Decision Problems
(DEC-POMDPs) have become a popular paradigm for multiagent
teamwork [2, 6, 8]. DEC-POMDPs are able to quantitatively express observational and action uncertainty, and yet optimally plan
communications and domain actions.
This paper focuses on teamwork under model uncertainty (i.e.,
potentially inaccurate transition and observation functions) in
DEC-POMDPs. In many domains, we only have an approximate
model of agent observation or transition functions. To address this
challenge we rely on execution-centric frameworks [7, 11, 12],
which simplify planning in DEC-POMDPs (e.g., by assuming costfree communication at plan-time), and shift coordination reasoning
to execution time. Specifically, during planning, these frameworks
have a standard single-agent POMDP planner [4] to plan a policy for the team of agents by assuming zero-cost communication.
Then, at execution-time, agents model other agents{\textquoteright} beliefs and actions, reason about when to communicate with teammates, reason
about what action to take if not communicating, etc. Unfortunately,
past work in execution-centric approaches [7, 11, 12] also assumes
a correct world model, and the presence of model uncertainty exposes key weaknesses that result in erroneous plans and additional
inefficiency due to reasoning over incorrect world models at every
decision epoch.
This paper provides two sets of contributions. The first is a
new execution-centric framework for DEC-POMDPs called MODERN (MOdel uncertainty in Dec-pomdp Execution-time ReasoNing). MODERN is the first execution-centric framework for DECPOMDPs explicitly motivated by model uncertainty. It is based on three key ideas: (i) it maintains an exponentially smaller model of
other agents{\textquoteright} beliefs and actions than in previous work and then further reduces the computation-time and space expense of this model
via bounded pruning; (ii) it reduces execution-time computation by
exploiting BDI theories of teamwork, thus limiting communication
to key trigger points; and (iii) it simplifies its decision-theoretic
reasoning about communication over the pruned model and uses a
systematic markup, encouraging extra communication and reducing uncertainty among team members at trigger points.
This paper{\textquoteright}s second set of contributions are in opening up model
uncertainty as a new research direction for DEC-POMDPs and
emphasizing the similarity of this problem to the Belief-DesireIntention (BDI) model for teamwork [5, 9]. In particular, BDI
teamwork models also assume inaccurate mapping between realworld problems and domain models. As a result, they emphasize
robustness via execution-time reasoning about coordination [9].
Given some of the successes of prior BDI research in teamwork,
we leverage insights from BDI in designing MODERN.},
	author = {Kwak, Jun-young and Yang, Rong and Yin, Zhengyu and Matthew E. Taylor and Tambe, Milind}
}
