Great advances have marked the progress of AI planning research over the past few years. Recent systems can quickly solve problems that are orders of magnitude harder than those tackled by the best previous planners. However, we are still a long way from understanding how humans plan. Understanding how humans plan is important if we are to develop intelligent planning systems capable of dealing...

Most planning problems have strong structures. They can be decomposed into subdomains with causal dependencies. The idea of exploiting the domain decomposition has motivated previous work such as hierarchical planning and factored planing. However, these algorithms require extensive backtracking and lead to few efficient general-purpose planners. On the other hand, heuristic search has been a s...

Case-based planning involves storing individual instances of problem-solving episodes and using them to tackle new planning problems. This paper is concerned with derivation replay, which is the main component of a form of case-based planning called derivational analogy (DA). Prior to this study, implementations of derivation replay have been based within state-space planning. We are motivated ...

One of the most promising trends in Domain Independent AI Planning, nowadays, is state-space heuristic planning. The planners of this category construct general but efficient heuristic functions, which are used as a guide to traverse the state space either in a forward or a in backward direction. Although specific problems may favor one or the other direction, there is no clear evidence why any...

Case-based planning involves storing individual instances of problem-solving episodes and using them to tackle new planning problems. This paper is concerned with derivation replay, which is the main component of a form of case-based planning called derivational analogy (DA). Prior to this study, implementations of derivation replay have been based within state-space planning. We are motivated ...

We describe the Zaroff system, a plan-based controller for the players in a game of hide and seek. The system features visually realistic human gure animation including realistic human locomotion. We discuss the planner's interaction with a changing environment to which it has only limited perceptual access. A hierarchical planner translates the game's goals of nding hiding players into locomot...

We will de ne states as descriptions of all environmental situations and operators as those things which transform one state into another. We will then introduce a state-space approach[4] and solve problems by conducting searches from the initial state to the goal state. Fig.3 represents applications of this framework to an intelligent manipulation system which can pick, place, slide, and dexte...