Some recent works in conditional planning have proposed reachability heuristics to improve planner scalability, but many lack a formal description of the properties of their distance estimates. To place previous work in context and extend work on heuristics for conditional planning, we provide a formal basis for distance estimates between belief states. We give a definition for the distance between belief states that relies on aggregating underlying state distance measures. We give several techniques to aggregate state distances and their associated properties. Many existing heuristics exhibit a subset of the properties, but in order to provide a standardized comparison we present several generalizations of planning graph heuristics that are used in a single planner. We compliment our belief state distance estimate framework by also investigating efficient planning graph data structures that incorporate BDDs to compute the most effective heuristics. We developed two planners to serve as test-beds for our investigation. The first, CAltAlt, is a conformant regression planner that uses A * search. The second, POND, is a conditional progression planner that uses AO * search. We show the relative effectiveness of our heuristic techniques within these planners. We also compare the performance of these planners with several

Sapa is a domain-independent heuristic forward chaining planner that can handle durative actions, metric resource constraints, and deadline goals. It is designed to be capable of handling the multi-objective nature of metric temporal planning. Our technical contributions include (i) planning-graph based methods for deriving heuristics that are sensitive to both cost and makespan (ii) techniques for adjusting the heuristic estimates to take action interactions and metric resource limitations into account and (iii) a linear time greedy post-processing technique to improve execution flexibility of the solution plans. An implementation of Sapa using many of the techniques presented in this paper was one of the best domain independent planners for domains with metric and temporal constraints in the third International Planning Competition, held at AIPS-02. We describe the technical details of extracting the heuristics and present an empirical evaluation of the current implementation of Sapa. 1.

In many real world planning scenarios, agents often do not have enough resources to achieve all of their goals. Consequently, they are forced to find plans that satisfy only a subset of the goals. Solving such partial satisfaction planning (PSP) problems poses several challenges, including an increased emphasis on modeling and handling plan quality (in terms of action costs and goal utilities). Despite the ubiquity of such PSP problems, very little attention has been paid to them in the planning community. In this paper, we start by describing a spectrum of PSP problems and focus on one of the more general PSP problems, termed PSP NET BEN-EFIT. We develop three techniques, (i) one based on integer programming, called OptiPlan, (ii) the second based on regression planning with reachability heuristics, called AltAlt ps, and (iii) the third based on anytime heuristic search for a forward state-space heuristic planner, called Sapa ps. Our empirical studies with these planners show that the heuristic planners generate plans that are comparable to the quality of plans generated by OptiPlan, while incurring only a small fraction of the cost.

We describe how to convert the heuristic search algorithm A * into an anytime algorithm that finds a sequence of improved solutions and eventually converges to an optimal solution. The approach we adopt uses weighted heuristic search to find an approximate solution quickly, and then continues the weighted search to find improved solutions as well as to improve a bound on the suboptimality of the current solution. When the time available to solve a search problem is limited or uncertain, this creates an anytime heuristic search algorithm that allows a flexible tradeoff between search time and solution quality. We analyze the properties of the resulting Anytime A * algorithm, and consider its performance in three domains; sliding-tile puzzles, STRIPS planning, and multiple sequence alignment. To illustrate the generality of this approach, we also describe how to transform the memoryefficient search algorithm Recursive Best-First Search (RBFS) into an anytime algorithm. 1.

The study of frameworks and formalisms for reasoning about action and change [67, 58, 61, 65, 70, 3, 57] has been central to the knowledge representation field almost from the inception of Artificial Intelligence as a general field of research [52, 56]. The phrase ”Temporal Action Logics ” represents a class of logics for reasoning about

This paper describes Marvin, a planner that competed in the Fourth International Planning Competition (IPC 4). Marvin uses action-sequence-memoisation techniques to generate macroactions, which are then used during search for a solution plan. We provide an overview of its architecture and search behaviour, detailing the algorithms used. We also empirically demonstrate the effectiveness of its features in various planning domains; in particular, the effects on performance due to the use of macro-actions, the novel features of its search behaviour, and the native support of ADL and Derived Predicates. 1.

Despite recent progress in AI planning, many problems remain challenging for current planners. In many domains, the performance of a planner can greatly be improved by discovering and exploiting information about the domain structure that is not explicitly encoded in the initial PDDL formulation. In this paper we present an automated method that learns relevant information from previous experience in a domain and uses it to solve new problem instances. Our approach produces a small set of useful macro-operators as a result of a training process. For each training problem, a structure called a solution graph is built based on the problem solution. Macro-operators with partial ordering of moves are extracted from the solution graph. A filtering and ranking procedure selects the most useful macro-operators, which will be used in future searches. We introduce a heuristic technique that uses only the most promising instantiations of a selected macro for node expansion. Our results indicate an impressive reduction of the search effort in complex domains where structure information can be inferred.

Many planning problems can be characterized as over-subscription problems in that goals have different utilities, actions have different costs and the planning system must choose a subset that will provide the greatest net benefit. This type of problems can not be solved by existing planning systems, where goals are assumed to have uniform utility, and the planner can terminate only when all of the goals are achieved. Existing methods for such problems use greedy approaches, which pre-select a subset of goals based on their estimated utility, and solve for those goals. Unfortunately, greedy methods, while efficient, can produce plans of arbitrarily low quality. In this paper, we introduce a more sophisticated heuristic search framework for over-subscription planning problems. In our framework, top-level goals are treated as soft-constraints and the search is guided by a relaxed-plan based heuristic that estimates the most beneficial set of goals from a given state. We implement this search framework in the context of Sapa, a forward state-space planner. We provide preliminary empirical results that demonstrate the effectiveness of our approach in comparison to a greedy approach. 1

Despite major progress in AI planning over the last few years, many interesting domains remain challenging for current planners. This paper presents component abstraction, an automatic and generic technique that can reduce the complexity of an important class of planning problems. Component abstraction uses static facts in a problem definition to decompose the problem into linked abstract components. A local analysis of each component is performed to speed up planning at the component level. Our implementation uses this analysis to statically build macro operators specific to each component. A dynamic filtering process keeps for future use only the most useful macro operators. We demonstrate our ideas in Depots, Satellite, and Rovers, three standard domains used in the third AI planning competition. Our results show an impressive potential for macro operators to reduce the search complexity and achieve more stable performance.

The development of informative, admissible heuristics for cost-optimal planning remains a significant challenge in domain-independent planning research. Two techniques are commonly used to try to improve heuristic estimates. The first is disjunction: taking the maximum across several heuristic values. The second is the use of additive techniques, taking the sum of the heuristic values from a set of evaluators in such a way that admissibility is preserved. In this paper, we explore how the two can be combined in a novel manner, using disjunction within additive heuristics. We define a general structure, the Additive–Disjunctive Heuristic Graph (ADHG), that can be used to define an interesting class of heuristics based around these principles. As an example of how an ADHG can be employed, and as an empirical demonstration, we then present a heuristic based on the well-known additive h m heuristic, showing an improvement in performance when additive–disjunctive techniques are used. 1