William Cushing

TIST Journal 2014 Journal Article

Learning Probabilistic Hierarchical Task Networks as Probabilistic Context-Free Grammars to Capture User Preferences

• Nan Li
• William Cushing
• Subbarao Kambhampati
• Sungwook Yoon

We introduce an algorithm to automatically learn probabilistic hierarchical task networks (pHTNs) that capture a user's preferences on plans by observing only the user's behavior. HTNs are a common choice of representation for a variety of purposes in planning, including work on learning in planning. Our contributions are twofold. First, in contrast with prior work, which employs HTNs to represent domain physics or search control knowledge, we use HTNs to model user preferences. Second, while most prior work on HTN learning requires additional information (e.g., annotated traces or tasks) to assist the learning process, our system only takes plan traces as input. Initially, we will assume that users carry out preferred plans more frequently, and thus the observed distribution of plans is an accurate representation of user preference. We then generalize to the situation where feasibility constraints frequently prevent the execution of preferred plans. Taking the prevalent perspective of viewing HTNs as grammars over primitive actions, we adapt an expectation-maximization (EM) technique from the discipline of probabilistic grammar induction to acquire probabilistic context-free grammars (pCFG) that capture the distribution on plans. To account for the difference between the distributions of possible and preferred plans, we subsequently modify this core EM technique by rescaling its input. We empirically demonstrate that the proposed approaches are able to learn HTNs representing user preferences better than the inside-outside algorithm. Furthermore, when feasibility constraints are obfuscated, the algorithm with rescaled input performs better than the algorithm with the original input.

AIJ Journal 2011 Journal Article

State agnostic planning graphs: deterministic, non-deterministic, and probabilistic planning

• Daniel Bryce
• William Cushing
• Subbarao Kambhampati

Planning graphs have been shown to be a rich source of heuristic information for many kinds of planners. In many cases, planners must compute a planning graph for each element of a set of states, and the naive technique enumerates the graphs individually. This is equivalent to solving a multiple-source shortest path problem by iterating a single-source algorithm over each source. We introduce a data-structure, the state agnostic planning graph, that directly solves the multiple-source problem for the relaxation introduced by planning graphs. The technique can also be characterized as exploiting the overlap present in sets of planning graphs. For the purpose of exposition, we first present the technique in deterministic (classical) planning to capture a set of planning graphs used in forward chaining search. A more prominent application of this technique is in conformant and conditional planning (i. e. , search in belief state space), where each search node utilizes a set of planning graphs; an optimization to exploit state overlap between belief states collapses the set of sets of planning graphs to a single set. We describe another extension in conformant probabilistic planning that reuses planning graph samples of probabilistic action outcomes across search nodes to otherwise curb the inherent prediction cost associated with handling probabilistic actions. Finally, we show how to extract a state agnostic relaxed plan that implicitly solves the relaxed planning problem in each of the planning graphs represented by the state agnostic planning graph and reduces each heuristic evaluation to counting the relevant actions in the state agnostic relaxed plan. Our experimental evaluation (using many existing International Planning Competition problems from classical and non-deterministic conformant tracks) quantifies each of these performance boosts, and demonstrates that heuristic belief state space progression planning using our technique is competitive with the state of the art.

SoCS Conference 2010 Conference Paper

Cost Based Search Considered Harmful

• William Cushing
• J. Benton 0001
• Subbarao Kambhampati

Planning research has returned to the issue of optimizing costs (rather than sizes) of plans. A prevalent perception, at least among non-experts in search, is that graph search for optimizing the size of paths generalizes more or less trivially to optimizing the cost of paths. While this kind of generalization is usually straightforward for graph theorems, graph algorithms are a different story. In particular, implementing a search evaluation function by substituting cost for size is a Bad Idea. Though experts have stated as much, cutting-edge practitioners are still learning of the consequences the hard way; here we mount a forceful indictment on the inherent dangers of cost-based search.

ICAPS Conference 2009 Conference Paper

Learning User Plan Preferences Obfuscated by Feasibility Constraints

• Nan Li
• William Cushing
• Subbarao Kambhampati
• Sung Wook Yoon

It has long been recognized that users can have complex preferences on plans. Non-intrusive learning of such preferences by observing the plans executed by the user is an attractive idea. Unfortunately, the executed plans are often not a true representation of user preferences, as they result from the interaction between user preferences and feasibility constraints. In the travel planning scenario, a user whose true preference is to travel by a plane may well be frequently observed traveling by car because of feasibility constraints (perhaps the user is a poor graduate student). In this work, we describe a novel method for learning true user preferences obfuscated by such feasibility constraints. Our base learner induces probabilistic hierarchical task networks (pHTNs) from sets of training plans. Our approach is to rescale the input so that it represents the user's preference distribution on plans rather than the observed distribution on plans.

IJCAI Conference 2007 Conference Paper

• William Cushing
• Mausam
• Subbarao Kambhampati
• Daniel Weld

While even STRIPS planners must search for plans of unbounded length, temporal planners must also cope with the fact that actions may start at any point in time. Most temporal planners cope with this challenge by restricting action start times to a small set of decision epochs, because this enables search to be carried out in state-space and leverages powerful state-based reachability heuristics, originally developed for classical planning. Indeed, decision-epoch planners won the International Planning Competition's Temporal Planning Track in 2002, 2004 and 2006. However, decision-epoch planners have a largely unrecognized weakness: they are incomplete. In order to characterize the cause of incompleteness, we identify the notion of required concurrency, which separates expressive temporal action languages from simple ones. We show that decision-epoch planners are only complete for languages in the simpler class, and we prove that the simple class is `equivalent' to STRIPS! Surprisingly, no problems with required concurrency have been included in the planning competitions. We conclude by designing a complete state-space temporal planning algorithm, which we hope will be able to achieve high performance by leveraging the heuristics that power decision epoch planners.

ICAPS Conference 2007 Conference Paper

Evaluating Temporal Planning Domains

• William Cushing
• Daniel S. Weld
• Subbarao Kambhampati
• Mausam
• Kartik Talamadupula

The last eight years have seen dramatic progress in temporal planning as highlighted by the temporal track in the last three International Planning Competitions (IPC). However, our recent work, (Cushing et al. 2007), showed that most of the competition winning planners are only complete for very restricted forms of temporal planning languages that are in a sense indistinguishable from STRIPS. In this paper we consider the impact of those results on the design of benchmark temporal planning domains, and by extension, the temporal planning competition. We start by setting out to verify our speculation that the competition domains are temporally simple. This turns out to be tricky, and we develop a set of increasingly powerful analytic methods for domain analysis. Our analysis establishes that the benchmark domains are indeed inherently sequential (i. e., do not require concurrency). We suggest some real-world domains with required concurrency, and use a compilation argument to show that these are harder in the sense that they correspond to longer sequential plans. We conclude with the argument that temporal planners should be evaluated on both inherently sequential domains as well as those requiring concurrency.

AAAI Conference 2005 Conference Paper

State Agnostic Planning Graphs and the Application to Belief-Space Planning

• William Cushing

Planning graphs have been shown to be a rich source of heuristic information for many kinds of planners. In many cases, planners must compute a planning graph for each element of a set of states. The naive technique enumerates the graphs individually. This is equivalent to solving an all-pairs shortest path problem by iterating a single-source algorithm over each source. We introduce a structure, the state agnostic planning graph, that directly solves the all-pairs problem for the relaxation introduced by planning graphs. The technique can also be characterized as exploiting the overlap present in sets of planning graphs. For the purpose of exposition, we first present the technique in classical planning. The more prominent application of this technique is in belief-space planning, where an optimization results in drastically improved theoretical complexity. Our experimental evaluation quantifies this performance boost, and demonstrates that heuristic belief-space progression planning using our technique is competitive with the state of the art.