MSDM 2015 - 10th Workshop on Multiagent Sequential Decision Making Under Uncertainty

In sequential decision making, an agent's objective is to choose actions based on observations of its environment that will maximize the expected performance over multiple steps. In worlds where actions are not deterministic or observations incomplete, Markov Decision Processes (MDPs) and Partially-Observable MDPs (POMDPs) serve as the basis for principled approaches to single-agent sequential decision making. Extending these models to systems of multiple agents has become the subject of an increasingly active area of research. Over the past decade, a variety of different multiagent models have emerged for cooperative agents (e.g., MMDP, MTDP and Dec-POMDP) and self-interested agents (e.g., I-POMDP and POSG), and under an assortment of different assumptions about agents' capabilities to communicate (e.g., Dec-MDP-Com, COM-MTDP), observe (e.g., Dec-MDP) and influence other agents (e.g., TI-Dec-MDP, ND-POMDP). The high computational complexity has driven researchers to develop multiagent planning and learning methods that exploit structure in agents' interactions, methods geared toward efficient approximate solutions, decentralized methods that distribute computation among the agents, and new ways for agents to model and reason about their interactions with other agents.
The purpose of this workshop is to bring together current and future researchers in the field of multiagent sequential decision making (MSDM) to present and discuss promising new work, to identify recent trends in model and algorithmic development, and to establish important directions and goals for further research and collaboration. This workshop also strives to develop consensus within the community on benchmarks and evaluation methodology in order to compare and validate alternative approaches and models. Further, we hope that these active discussions and collaborations will help us to overcome the challenges of successfully applying MSDM methods to real-world problems in security, sustainability, public safety and health, and other challenging domains.
The MSDM workshop will also include a short tutorial that will briefly review how to make optimal and approximately optimal decisions in multiagent settings of cooperative or adversarial nature. Departing from game theory, the tutorial will visit the major models and solution techniques for sequential decision making under uncertainty in the context of multiagent interactions of increasing generality. The tutorial will acquaint the workshop audience with the appropriate background and historical perspective to understand and appreciate the ensuing talks and discussions.
Topics
Multiagent sequential decision making comprises (1) problem representation, (2) planning, (3) coordination, and (4) learning. The MSDM workshop addresses this full range of aspects. Topics of particular interest include:
Fundamental modeling challenges, e.g.,
model specification: how should models be derived?
model granularity: how should one decide on an appropriate level of abstraction to express decision-making models?
Novel representations, algorithms and complexity results
Comparisons of algorithms
Relationships between models and their assumptions
Decentralized vs. centralized planning approaches
Online vs. offline planning
Communication and coordination during execution
Computational issues involving…
large numbers of agents
large numbers of discrete / continuous states, observations and actions
long decision horizons
(Reinforcement) learning in partially observable multiagent systems
Cooperative, competitive, and self-interested agents
Application domains
Benchmarks and evaluation methodologies
Standardization of software
High-level principles in MSDM: past trends and future directions