David Speck – Publications

In classical planning, the task is to derive a sequence of deterministic actions that changes the current fully-observable world state into one that satisfies a set of goal criteria. Algorithms for classical planning are domain-independent, i.e., they are not limited to a particular application and instead can be used to solve different types of reasoning problems. The main language for modeling such problems is the Planning Domain Definition Language (PDDL). Even though it provides many language features for expressing a wide range of planning tasks, most of today’s classical planners, especially optimal ones, support only a small subset of its features. The most widely supported fragment is lifted STRIPS plus types and action costs. While this fragment suffices to model some interesting planning tasks, using it to model more realistic problems often incurs a much higher modeling effort. Even if modeling is possible at all, solving the resulting tasks is often infeasible in practice, as the required encoding size increases exponentially.

To address these issues, we show how to support more expressive modeling languages natively in optimal classical planning algorithms. Specifically, we focus on symbolic search, a state-of-the-art search algorithm that operates on sets of world states. We show how to extend symbolic search to support classical planning with conditional effects, axioms, and state- dependent action costs. All of these modeling features are expressive in the sense that compiling them away incurs a significant blow-up, so is it often necessary to support them natively. Except for blind (non-symbolic) search, our new symbolic search is the first optimal classical planning algorithm that supports these three modeling extensions in combination, and it even compares favorably to other state-of-the-art approaches that only support a subset of the extensions.

Classical planning asks for a sequence of operators reaching a given goal. While the most common case is to compute a plan, many scenarios require more than that. However, quantitative reasoning on the plan space remains mostly unexplored. A fundamental problem is to count plans, which relates to the conditional probability on the plan space. Indeed, qualitative and quantitative approaches are well-established in various other areas of automated reasoning.

We present the first study to quantitative and qualitative reasoning on the plan space. In particular, we focus on polyno mially bounded plans. On the theoretical side, we study its complexity, which gives rise to rich reasoning modes. Since counting is hard in general, we introduce the easier notion of facets, which enables understanding the significance of oper ators. On the practical side, we implement quantitative reasoning for planning. Thereby, we transform a planning task into a propositional formula and use knowledge compilation to count different plans. This framework scales well to large plan spaces, while enabling rich reasoning capabilities such as learning pruning functions and explainable planning.

Automated problem reformulation is a common technique in classical planning to identify and exploit problem structures. Decoupled search is an approach that automatically decomposes planning tasks based on their causal structure, often significantly reducing the search effort. However, its broad applicability is limited by the need for specialized algorithms. In this paper, we present an approach that embodies decoupled search for non-optimal planning through a novel task transformation. Specifically, given a task and a decomposition, we create a transformed task such that the state space of the transformed task is isomorphic to that of decoupled search on the original task. This eliminates the need for specialized algorithms and allows the use of various planning technology in the decoupled-search framework. Empirical evaluation shows that our method is empirically competitive with specialized decoupled algorithms and favorable to other related problem reformulation techniques.

Saturated post-hoc optimization is a powerful method for computing admissible heuristics for optimal classical planning. The approach solves a linear program (LP) for each state encountered during the search, which is computationally demanding. In this paper, we theoretically and empirically analyze to which extent we can reuse an LP solution of one state for another. We introduce a novel sensitivity analysis that can exactly characterize the set of states for which a unique LP solution is optimal. Furthermore, we identify two properties of the underlying LPs that affect reusability. Finally, we introduce an algorithm that optimizes LP solutions to generalize well to other states. Our new algorithms significantly reduce the number of necessary LP computations.

State-space reduction techniques are a powerful means to enhancing the performance of search-based planners. Recent work has shown that decoupled search, which compactly represents sets of states, can be cast as a task reformulation. Concretely, it is possible to exactly simulate the behavior and reduction of (non-optimal) decoupled search by applying a transformation to the input task and running regular search on the transformed task. In this work, we investigate if the same approach is feasible for symmetry breaking, in particular orbit space search (OSS). One of the challenges in this endeavor is that OSS dynamically computes a canonical representative of every state generated during search. To represent this computation as a task transformation, however, we need to fix the procedure at transformation time. This leads to reduced pruning potential, which we discuss in detail and verify empirically. We also discuss an approach that can fully simulate OSS, at the cost of vastly blowing-up the task size.