We describe the ucpop partial order planning algorithm which handles a subset of Pednault's ADL action representation. In particular, ucpop operates with actions that have conditional effects, universally quantified preconditions and effects, and with universally quantified goals. We prove ucpop is both sound and complete for this representation and describe a practical implementation that succeeds on all of Pednault's and McDermott's examples, including the infamous "Yale Stacking Problem" [McDermott 1991].

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We define the probabilistic planning problem in terms of a probability distribution over initial world states, a boolean combination of propositions representing the goal, a probability threshold, and actions whose effects depend on the execution-time state of the world and on random chance. Adopting a probabilistic model complicates the definition of plan success: instead of demanding a plan that provably achieves the goal, we seek plans whose probability of success exceeds the threshold. In this paper, we present buridan, an implemented least-commitment planner that solves problems of this form. We prove that the algorithm is both sound and complete. We then explore buridan's efficiency by contrasting four algorithms for plan evaluation, using a combination of analytic methods and empirical experiments. We also describe the interplay between generating plans and evaluating them, and discuss the role of search control in probabilistic planning. 3 We gratefully acknowledge the comment...

We describe an extension of graphplan to a subset of ADL that allows conditional and universally quantified effects in operators in such away that almost all interesting properties of the original graphplan algorithm are preserved.

A fundamental assumption made by classical AI planners is that there is no uncertainty in the world: the planner has full knowledge of the conditions under which the plan will be executed and the outcome of every action is fully predictable. These planners cannot therefore construct contingency plans, i.e., plans in which different actions are performed in different circumstances. In this paper we discuss some issues that arise in the representation and construction of contingency plans and describe Cassandra, a partial-order contingency planner. Cassandra uses explicit decision-steps that enable the agent executing the plan to decide which plan branch to follow. The decision-steps in a plan result in subgoals to acquire knowledge, which are planned for in the same way as any other subgoals. Cassandra thus distinguishes the process of gathering information from the process of making decisions. The explicit representation of decisions in Cassandra allows a coherent approach to...

This paper postulates a rigorous method for the construction of classical planning domain models. We describe, with the help of a non-trivial example, a tool supported method for encoding such models. The method results in an `object-centred' specification of the domain that lifts the representation from the level of the literal to the level of the object. Thus, for example, operators are defined in terms of how they change the state of objects, and planning states are defined as amalgams of the objects' states. The method features two classes of tools: for initial capture and validation of the domain model; and for operationalising the domain model (a process we call compilation) for later planning. Here we focus on compilation tools used to generate macros and goal orders to be utilised at plan generation time. We describe them in depth, and evaluate empirically their combined benefits in plan-generation speed-up. The method's main benefit is in helping the modeller to pro...

We present a plan representation and a generalized algorithm template, called UCP, for unifying the classical plan-space and state-space planning approaches within a single framework. UCP models planning as a process of refining a partial plan. The plan-space and state-space planning approaches are cast as complementary refinement strategies operating on the same partial plan representation. UCP has the freedom to arbitrarily and opportunistically interleave plan-space and state-space refinements within a single planning episode. This allows it reap the benefits of both state-space and plan-space planning approaches. We discuss the coverage, completeness and systematicity of UCP. We also present some preliminary empirical results that demonstrate the utility of combining state-space and plan-space approaches. 1 Introduction Domain independent classical planning techniques fall into two broad categories-- state space planners which search in the space of states, and plan space planners...

Given the intractability of domain-independent planning, the ability to control the search of a planner is vitally important. One way of doing this involves learning from search failures. This paper describes SNLP+EBL, the first implementation of explanation based search control rule learning framework for a partial order (plan-space) planner. We will start by describing the basic learning framework of SNLP+EBL. We will then concentrate on SNLP+EBL's ability to learn from failures, and describe the results of empirical studies which demonstrate the effectiveness of the search-control rules SNLP+EBL learns using our method. We then

Recent work on foundations of partial-order planning has emphasized the importance of systematicity and elimination of redundancy in search space as a way to improve planning performance. In this paper, we investigate the utility of systematicity. Starting with a a rational reconstruction of the motivations for systematicity, we will conclude that it eliminates redundancy in the search space at the expense of increased commitment during planning. This increase in commitment leads to higher backtracking and increased solution depth, both of which can adversely affect the performance of the planner. We will argue that the performance of a planner is correlated more closely with the way it balances the tradeoff between redundancy and commitment, than with the systematicity of its search. We will discuss a spectrum of solutions for dealing with the redundancy-commitment tradeoff and show that systematic planners are at one extreme of this spectrum, with total least commitment planners like...

Chapman's paper, "Planning for Conjunctive Goals," has been widely acknowledged for its contribution toward understanding the nature of partial-order planning, and it has been one of the bases of later work by others---but it is not free of problems. This paper addresses some problems involving modal truth and the Modal Truth Criterion (MTC). Our results are as follows: (i) Even though modal duality is a fundamental property of classical modal logics, it does not hold for modal truth in Chapman's plans; i.e., "necessarily p" is not equivalent to "not possibly :p." (ii) Although the MTC for necessary truth is correct, the MTC for possible truth is incorrect: it provides necessary but insufficient conditions for ensuring possible truth. Furthermore, even though necessary truth can be determined in polynomial time, possible truth is NP-hard. (iii) If we rewrite the MTC to talk about modal conditional truth (i.e., modal truth conditional on executability) rather than modal truth, then bot...