Many traditional solution approaches to relationally specified decision-theoretic planning
problems (eg, those stated in the probabilistic planning domain description language, or …

Stochastic Shortest Path (SSP) MDPs is a problem class widely studied in AI, especially in
probabilistic planning. They describe a wide range of scenarios but make the restrictive …

UCT, the premier method for solving games such as Go, is also becoming the dominant
algorithm for probabilistic planning. Out of the five solvers at the International Probabilistic …

An original reinforcement learning (RL) methodology is proposed for the design of multi-
agent systems. In the realistic setting of situated agents with local perception, the task of …

Past approaches for solving MDPs have several weaknesses: 1) Decision-theoretic
computation over the state space can yield optimal results but scales poorly. 2) Value …

Abstract The Factored Policy-Gradient planner (FPG)(Buffet & Aberdeen 2006) was a
successful competitor in the probabilistic track of the 2006 International Planning …

It is cooperation that essentially differentiates multi-agent systems (MASs) from single-agent
intelligence. In realistic MAS applications such as RoboCup, repeated work has shown that …

自动规划针对特定领域的特定问题, 生成一个由可应用动作构成的规划. 经典规划中的动作效果
是确定的, 且在每个时间步内只能执行一个动作. 但在实际问题中, 动作的效果往往是不确定性的 …

We propose a new approach for solving a class of discrete decision making problems under
uncertainty with positive cost. This issue concerns multiple and diverse fields such as …

Location-based services are widely spread both as entertainment and business
applications. The focus of this work is on one particular area–tourist-oriented information …