We present a heuristic search algorithm for solving first-order Markov
Decision Processes (FOMDPs). Our approach combines first-order state
abstraction that avoids evaluating states individually, and heuristic search
that avoids evaluating all states. Firstly, in contrast to existing systems,
which start with propositionalizing the FOMDP and then perform state
abstraction on its propositionalized version we apply state abstraction
directly on the FOMDP avoiding propositionalization. This kind of abstraction
is referred to as first-order state abstraction. Secondly, guided by an
admissible heuristic, the search is restricted to those states that are
reachable from the initial state. We demonstrate the usefulness of the above
techniques for solving FOMDPs with a system, referred to as FluCaP (formerly,
FCPlanner), that entered the probabilistic track of the 2004 International
Planning Competition (IPC2004) and demonstrated an advantage over other
planners on the problems represented in first-order terms.

该论文提出了一种启发式搜索算法来解决一阶马尔可夫决策过程，其方法结合了一阶状态抽象和启发式搜索，以避免对所有状态进行评估，并通过可达性引导来限制搜索。该方法在国际计划竞赛中取得了较好的成绩。