For more details, see Nau et al. 's preconditions can include logical inferences, 's preconditions two methods for traveling from one location can include Horn-clause inferencing, numeric to another: (1) traveling by airplane and (2) computations, and calls to external programs. 's expressive power can be used to create a totally ordered list of subtasks. Suppose domain representations for complex application that all these subtasks are primitive except for domains. For example, the Horn 4. if t is primitive (i.e., there is an operator for t) then clauses can include calls to attached procedures 5. nondeterministically choose an operator o for t We believe the primary 14. endif's higher level of expressivity made it possible to formulate highly expressive domain algorithms in's data structures to make them faster; for example, we found that a simple change to the data structure We intend to make more optimizations in the near future. (Aha and Breslow 1997).

Mips is a planning system that applies binary decision diagrams (BDDs) to compactly represent world states in a planning problem and efficiently explore the underlying state space. It was the first general planning system based on model-checking methods. It can handle the strips subset of the pddl language and some additional features from adl, namely, negative preconditions and (universal) conditional effects. At the Fifth International Conference on Artificial Intelligence Planning and Scheduling (AIPS'00), mips was one of five planning systems to be awarded for distinguished performance in the fully automated track. This article gives a brief introduction to, and explains the basic planning algorithm used by, mips, using a simple logistics problem as an example.

The main idea that arise during the forward search phase and of the planner is to compute offline, in the preprocessing the goals. This approach succeeds in the notion of related facts in the goal-regression avoiding computing estimates for invalid facts process. These are facts that have been achieved in the preprocessing phase. However, it introduces either by the same or subsequent actions, without some problems in situations where the the last actions deleting the facts achieved goal state is not completely described because first. The cost of achieving simultaneously a set an action to regress the goals might not exist. of unrelated facts is considered equal to the To cope with this situation, at the beginning sum of their individual costs, whereas the cost of the preprocessing phase, We know from our experience that if move actions were Table 1.

Fast-forward (FF) was the most successful automatic planner in the Fifth International Conference on Artificial Intelligence Planning and Scheduling (AIPS '00) planning systems competition. Like the well-known hsp system, FF relies on forward search in the state space, guided by a heuristic that estimates goal distances by ignoring delete lists. It differs from HSP in a number of important details. This article describes the algorithmic techniques used in FF in comparison to hsp and evaluates their benefits in terms of run-time and solution-length behavior.

Other achieves a tradeoff between optimality and heuristic search planners in the AIPS2000 Contest speed. The transition function f maps states s file domain.pddl. The syntax for the instance into states s′ s - Del(a) Add(a) for and domain files is given by the PDDL standard. The action costs c(a) are all equal to 1. that includes The states s S are sets of atoms from can also choose a schedule of options as done A. in The set of actions A(s) applicable in s is found. In addition, the different options can are the operators op in O that are relevant be run concurrently as threads.

All subsequent work considers the obtained Petri net representation. These tokens hold on ction planning is generally done in two (2) searching it for a solution. The way a label of the specific bindings of the variables work load is shared between these stages of the predicate associated with the place. The particularity of our planner lays in the flexibility it offers in the this listing. Indeed, all markings reachable way it builds the search space, which, in turn, in one step are unified in one sole marking: leads to valuable consequences over the search They are superposed, and copies of itself.

Planning domains often feature subproblems such as route planning and resource handling. Using static domain analysis techniques, we have been able to identify certain commonly occurring subproblems within planning domains, making it possible to abstract these subproblems from the overall goals of the planner and deploy specialized technology to handle them in a way integrated with the broader planning activities. Using two such subsolvers our hybrid planner, stan4, participated successfully in the Fifth International Conference on Artificial Intelligence Planning and Scheduling (AIPS'00) planning competition.

The planning competition has become a regular part of the biennial Artificial Intelligence Planning and Scheduling (AIPS) conferences. AIPS'98 featured the very first competition, and for AIPS'00, we built on this foundation to run the second competition. The 2000 competition featured a much larger group of participants and a wide variety of different approaches to planning. Some of these approaches were refinements of known techniques, and others were quite different from anything that had been tried before. Besides the dramatic increase in participation, the 2000 competition demonstrated that planning technology has taken a giant leap forward in performance since 1998. The 2000 competition featured planning systems that were orders of magnitude faster than the planners of just two years prior. This article presents an overview of the competition and reviews the main results.

Domain-independent planning is a hard combinatorial problem. Taking into account plan quality makes the task even more difficult. This article introduces Planning by Rewriting (PbR), a new paradigm for efficient high-quality domain-independent planning. PbR exploits declarative plan-rewriting rules and efficient local search techniques to transform an easy-to-generate, but possibly suboptimal, initial plan into a high-quality plan. In addition to addressing the issues of planning efficiency and plan quality, this framework offers a new anytime planning algorithm. We have implemented this planner and applied it to several existing domains. The experimental results show that the PbR approach provides significant savings in planning effort while generating high-quality plans.

This paper presents GRT, a domain-independent heuristic planning system for STRIPS worlds. GRT solves problems in two phases. In the pre-processing phase, it estimates the distance between each fact and the goals of the problem, in a backward direction. Then, in the search phase, these estimates are used in order to further estimate the distance between each intermediate state and the goals, guiding so the search process in a forward direction and on a best-first basis. The paper presents the benefits from the adoption of opposite directions between the preprocessing and the search phases, discusses some difficulties that arise in the pre-processing phase and introduces techniques to cope with them. Moreover, it presents several methods of improving the efficiency of the heuristic, by enriching the representation and by reducing the size of the problem. Finally, a method of overcoming local optimal states, based on domain axioms, is proposed. According to it, difficult problems are decomposed into easier sub-problems that have to be solved sequentially. The performance results from various domains, including those of the recent planning competitions, show that GRT is among the fastest planners.