Partially Synchronized DEC-MDPs in Dynamic Mechanism Design
نویسندگان
چکیده
In this paper, we combine for the first time the methods of dynamic mechanism design with techniques from decentralized decision making under uncertainty. Consider a multi-agent system with self-interested agents acting in an uncertain environment, each with private actions, states and rewards. There is also a social planner with its own actions, rewards, and states, acting as a coordinator and able to influence the agents via actions (e.g., resource allocations). Agents can only communicate with the center, but may become inaccessible, e.g., when their communication device fails. When accessible to the center, agents can report their local state (and models) and receive recommendations from the center about local policies to follow for the present period and also, should they become inaccessible, until becoming accessible again. Without self-interest, this poses a new problem class which we call partially-synchronized DEC-MDPs, and for which we establish some positive complexity results under reasonable assumptions. Allowing for self-interested agents, we are able to bridge to methods of dynamic mechanism design, aligning incentives so that agents truthfully report local state when accessible and choose to follow the prescribed “emergency policies” of the center.
منابع مشابه
Dec-POMDPs as Non-Observable MDPs
A recent insight in the field of decentralized partially observable Markov decision processes (Dec-POMDPs) is that it is possible to convert a Dec-POMDP to a non-observable MDP, which is a special case of POMDP. This technical report provides an overview of this reduction and pointers to related literature.
متن کاملApplications of DEC-MDPs in multi-robot systems
Optimizing the operation of cooperative multi-robot systems that can cooperatively act in large and complex environments has become an important focal area of research. This issue is motivated by many applications involving a set of cooperative robots that have to decide in a decentralized way how to execute a large set of tasks in partially observable and uncertain environments. Such decision ...
متن کاملProbabilistic Planning with Risk-Sensitive Criterion
Probabilistic planning models and, in particular, Markov Decision Processes (MDPs), Partially Observable Markov Decision Processes (POMDPs) and Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) have been extensively used by AI and Decision Theoretic communities for planning under uncertainty. Typically, the solvers for probabilistic planning models find policies that min...
متن کاملEvaluation of Batch-Mode Reinforcement Learning Methods for Solving DEC-MDPs with Changing Action Sets
DEC-MDPs with changing action sets and partially ordered transition dependencies have recently been suggested as a sub-class of general DEC-MDPs that features provably lower complexity. In this paper, we investigate the usability of a coordinated batch-mode reinforcement learning algorithm for this class of distributed problems. Our agents acquire their local policies independent of the other a...
متن کاملExploiting separability in multiagent planning with continuous-state MDPs
Recent years have seen significant advances in techniques for optimally solving multiagent problems represented as decentralized partially observable Markov decision processes (Dec-POMDPs). A new method achieves scalability gains by converting Dec-POMDPs into continuous state MDPs. This method relies on the assumption of a centralized planning phase that generates a set of decentralized policie...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2008