Conformant Planning Research Articles

A Compilation Based Approach to Conformant Probabilistic Planning with Stochastic Actions

We extend RBPP, the state-of-the-art, translation-based planner for conformant probabilistic planning (CPP) with deterministic actions,to handle a wide set of CPPs with stochastic actions. Our planner uses relevance analysis to divide a probabilistic "failure-allowance" between the initial state and the stochastic actions. Using its "initial-state allowance," it uses relevance analysis to select a subset of the set of initial states on which planning efforts will focus. Then, it generates a deterministic planning problem using all-outcome determinization in which action cost reflects the probability of the modeledoutcome. Finally, a cost-bounded classical planner generates a plan with failure probability lower than the"stochastic-effect allowance." Our compilation method is sound, but incomplete, as it may underestimates the success probability of a plan. Yet, it scales up much better than the state-of-the-art PFF planner, solving larger problems and handling tighter probabilistic bounds on existing benchmarks.

A Relevance-Based Compilation Method for Conformant Probabilistic Planning

Conformant probabilistic planning (CPP) differs from conformant planning (CP) by two key elements: the initial belief state is probabilistic,and the conformant plan must achieve the goal with probability $\geq\theta$, for some $0<\theta\leq 1$. In earlier work we observed that one can reduce CPP to CP by finding a set of initial states whose probability $\geq\theta$, for whicha conformant plan exists. In previous solvers we used the underlying planner to select this set of states and to plan for them simultaneously. Here we suggest an alternative approach: start with relevance analysis to determine a promising set of initial states on which to focus. Then, call an off-the-shelf conformant planner to solve the resulting problem. This approach has a number of advantages. First, instead of depending on the heuristic function to select the set of initial states,we can introduce specific, efficient relevance reasoning techniques. Second, we can benefit from optimizations used by conformant planners that are unsound when applied to the original CPP. Finally, we are free to use any existing (or new) CP solver. Consequently, the new planner dominates previous solvers on almost all domains and scales to instances that were not solved before.

Compilation Based Approaches to Probabilistic Planning -- Thesis Summary

The main focus of our work is the use of classical planning algorithms in service of more complex problems of planning under uncertainty. In particular, we are exploring compilation techniques that allow us to reduce some probabilistic planning problems into variants of classical planning, such as metric planning,resource-bounded planning, and cost-bounded suboptimal planning. Currently, our initial work focuses on \emph{conformant probabilistic planning}. We intend toimprove our current methods by improving our compilation methods, but also by improving the ability of current planners to handle the special features ofour compiled problems. Then, we hope to extend these techniques to handle more complex probabilistic settings, such as problems with stochastic actions andpartial observability.

Partially Observable Online Contingent Planning Using Landmark Heuristics

In contingent planning problems, agents have partial information about their state anduse sensing actions to learn the value of some variables.When sensing and actuation are separated, plans for such problems can often be viewed as a tree of sensing actions, separated by conformant plans consisting of non-sensing actions that enable the execution of the next sensing action. This leads us to propose a heuristic, online method for contingent planning which focuses on identifying thenext useful sensing action. The key part of our planner is a novel landmarks-based heuristic for selecting the next sensing action, together with a projection method that uses classical planning to solve the intermediate conformant planning problems.This allows our planner to operate without an explicit model of belief space or the use of existing translation techniques,both of which can require exponential space. The resulting Heuristic Contingent Planner (HCP) solves many more problems than state-of-the-art, translation-based online contingent planners, and in most cases much faster.

Conformant Planning Based on Reducing Belief States

Conformant 规划问题通常转化为信念状态空间的搜索问题来求解.提出了通过降低信念状态的不确定性来提高规划求解效率的方法.首先给出缩减信念状态的增强爬山算法,在此基础上,提出了基于缩减信念状态的Conformant 规划方法,设计了CFF-Lite 规划系统.该规划器的求解过程包括两次增强爬山过程,分别用于缩减信念状态和搜索目标.首先对初始信念状态作最大程度的缩减,提高启发函数的准确性;然后从缩减后的信念状态开始执行启发式搜索.实验结果表明,CFF-Lite 规划系统通过快速缩减信念状态降低了问题的求解难度,在大多数问题上,求解效率和规划解质量与Conformant-FF 相比,都有显著的提高.

Assumption-Based Planning: Generating Plans and Explanations under Incomplete Knowledge

Many practical planning problems necessitate the generation of a plan under incomplete information about the state of the world. In this paper we propose the notion of Assumption-Based Planning. Unlike conformant planning, which attempts to find a plan under all possible completions of the initial state, an assumption-based plan supports the assertion of additional assumptions about the state of the world, often resulting in high quality plans where no conformant plan exists. We are interested in this paradigm of planning for two reasons: 1) it captures a compelling form of \emph{commonsense planning}, and 2) it is of great utility in the generation of explanations, diagnoses, and counter-examples -- tasks which share a computational core with We formalize the notion of assumption-based planning, establishing a relationship between assumption-based and conformant planning, and prove properties of such plans. We further provide for the scenario where some assumptions are more preferred than others. Exploiting the correspondence with conformant planning, we propose a means of computing assumption-based plans via a translation to classical planning. Our translation is an extension of the popular approach proposed by Palacios and Geffner and realized in their T0 planner. We have implemented our planner, A0, as a variant of T0 and tested it on a number of expository domains drawn from the International Planning Competition. Our results illustrate the utility of this new planning paradigm.

Compiling Conformant Probabilistic Planning Problems into Classical Planning

In CPP, we are given a set of actions (assumed deterministic in this paper), a distribution over initial states, a goal condition, and a real value 0 < θ ≤1. We seek a plan π such that following its execution, the goal probability is at least θ. Motivated by the success of the translation-based approach for conformant planning, introduced by Palacios and Geffner, we suggest a new compilation scheme from CPP to classical planning. Our compilation scheme maps CPP into cost-bounded classical planning, where the cost-bound represents the maximum allowed probability of failure. Empirically, this technique shows mixed, but promising results, performing very well on some domains, and less so on others when compared to the state of the art PFF planner. It is also very flexible due to its generic nature, allowing us to experiment with diverse search strategies developed for classical planning. Our results show that compilation-based technique offer a new viable approach to CPP and, possibly, more general probabilistic planning problems.

A Novel Heuristic Usage of Helpful Actions for Conformant-FF System

Conformant planning is usually transformed into a search problem in the space of belief states, where the combinatorial explosion of search space has been one of the most intractable problems. In this paper, we present a novel usage of the helpful action pruning technique in the Conformant-FF planner. The key idea is to change the way it deals with helpful actions and first consider actions from the so-called implication path which was used by Conformant-FF for concluding which subgoal would be considered known to be true in the relaxed planning graph. We first point out the semantics of solving by cases, indicated by the implication paths of the relaxed planning process. In line with the semantics, we then propose our heuristic idea of using these implication paths further by attempting to collect certain groups of helpful actions such that executing all actions within a group can achieve some subgoal while executing an individual action in the group can not due to incomplete information. This technique usually leads to the goal faster and cuts down the search space dramatically. We evaluate the idea experimentally. In a number of conformant benchmarks, our heuristic pruning technique outperforms helpful actions pruning in both planning efficiency and the size of search space.

Translation Algorithm for Negative Literals in Conformant Planning

The encoded negative literals in a conformant planning task will result in increasing state spaces. Getting a compact representation of state spaces is one of the most important issues in conformant planning. In this paper, a translation algorithm for negative literals is proposed to reduce the state spaces in a conformant planning task. The relationship between encoded literals is analyzed in detail. Based on the one-of relaxation technique in domain language, the algorithm is used to express the uncertain initial states and action effects in conformant planning. It converts formula one-of into a set of mutually exclusive literals with the relationship of mutual. The experiment study shows the efficiency of the proposed algorithm in pruning the state space in conformant planning tasks.

An approximative inference method for solving ∃∀SO satisfiability problems

This paper considers the fragment ∃∀SO of second-order logic. Many interesting problems, such as conformant planning, can be naturally expressed as finite domain satisfiability problems of this logic. Such satisfiability problems are computationally hard (ΣP2) and many of these problems are often solved approximately. In this paper, we develop a general approximative method, i.e., a sound but incomplete method, for solving ∃∀SO satisfiability problems. We use a syntactic representation of a constraint propagation method for first-order logic to transform such an ∃∀SO satisfiability problem to an ∃SO(ID) satisfiability problem (second-order logic, extended with inductive definitions). The finite domain satisfiability problem for the latter language is in NP and can be handled by several existing solvers. Inductive definitions are a powerful knowledge representation tool, and this moti- vates us to also approximate ∃∀SO(ID) problems. In order to do this, we first show how to perform propagation on such inductive definitions. Next, we use this to approximate ∃∀SO(ID) satisfiability problems. All this provides a general theoretical framework for a number of approximative methods in the literature. Moreover, we also show how we can use this framework for solving practical useful problems, such as conformant planning, in an effective way.

Mutually Exclusive Decision for Concurrent Actions in Conformant Planning

Mutually Exclusive Decision for Concurrent Actions in Conformant Planning

On Computing Conformant Plans Using Classical Planners: A Generate-And-Complete Approach

The paper illustrates a novel approach to conformant planning using classical planners. The approach relies on two core ideas developed to deal with incomplete information in the initial situation: the use of a classical planner to solve non-classical planning problems, and the reduction of the size of the initial belief state. Differently from previous uses of classical planners to solve non-classical planning problems, the approach proposed in this paper creates a valid plan from a possible plan---by inserting actions into the possible plan and maintaining only one level of non-deterministic choice (i.e., the initial plan being modified). The algorithm can be instantiated with different classical planners---the paper presents the GC[LAMA] implementation, whose classical planner is LAMA. We investigate properties of the approach, including conditions for completeness. GC[LAMA] is empirically evaluated against state-of-the-art conformant planners, using benchmarks from the literature. The experimental results show that GC[LAMA] is superior to other planners, in both performance and scalability. GC[LAMA] is the only planner that can solve the largest instances from several domains. The paper investigates the reasons behind the good performance and the challenges encountered in GC[LAMA].

Enhanced Symmetry Breaking in Cost-Optimal Planning as Forward Search

The paper illustrates a novel approach to conformant planning using classical planners. The approach relies on two core ideas developed to deal with incomplete information in the initial situation: the use of a classical planner to solve non-classical planning problems, and the reduction of the size of the initial belief state. Differently from previous uses of classical planners to solve non-classical planning problems, the approach proposed in this paper creates a valid plan from a possible plan---by inserting actions into the possible plan and maintaining only one level of non-deterministic choice (i.e., the initial plan being modified). The algorithm can be instantiated with different classical planners---the paper presents the GC[LAMA] implementation, whose classical planner is LAMA. We investigate properties of the approach, including conditions for completeness. GC[LAMA] is empirically evaluated against state-of-the-art conformant planners, using benchmarks from the literature. The experimental results show that GC[LAMA] is superior to other planners, in both performance and scalability. GC[LAMA] is the only planner that can solve the largest instances from several domains. The paper investigates the reasons behind the good performance and the challenges encountered in GC[LAMA].

PHASE TRANSITIONS OF EXPSPACE-COMPLETE PROBLEMS: A FURTHER STEP

This paper further explores the phase transitions of the EXPSPACE-complete problems, focusing on the conformant plan modification. By analyzing features of the conformant plan modification, quantitative results are obtained. If the number of actions is't greater than θub, almost all the conformant planning instances can't be solved with the conformant plan modification. If the number of actions is't lower than θlb, almost all the conformant planning instances can be solved with the conformant plan modification. The results of the experiments show that there indeed exists an experimental threshold γc of density (ratio of number of actions to number of propositions), which separates the region where almost all conformant planning instances can't be solved with the conformant plan modification from the region where almost all conformant planning instances can be solved with the conformant plan modification.

On the Effectiveness of Belief State Representation in Contingent Planning

This work proposes new approaches to contingent planning using alternative belief state representations extended from those in conformant planning and a new AND/OR forward search algorithm, called PrAO, for contingent solutions. Each representation was implemented in a new contingent planner. The important role of belief state representation has been confirmed by the fact that our planners all outperform other stateof- the-art planners on most benchmarks and the comparison of their performances varies across all the benchmarks even using the same search algorithm PrAO and same unsophisticated heuristic scheme. The work identifies the properties of each representation method that affect the performance.

Conjunctive Representations in Contingent Planning: Prime Implicates Versus Minimal CNF Formula

This paper compares in depth the effectiveness of two conjunctive belief state representations in contingent planning: prime implicates and minimal CNF, a compact form of CNF formulae, which were initially proposed in conformant planning research (To et al. 2010a; 2010b). Similar to the development of the contingent planner CNFct for minimal CNF (To et al. 2011b), the present paper extends the progression function for the prime implicate representation in (To et al. 2010b) for computing successor belief states in the presence of incomplete information to handle non-deterministic and sensing actions required in contingent planning. The idea was instantiated in a new contingent planner, called PIct, using the same AND/OR search algorithm and heuristic function as those for CNFct. The experiments show that, like CNFct, PIct performs very well in a wide range of benchmarks. The study investigates the advantages and disadvantages of the two planners and identifies the properties of each representation method that affect the performance.

Effective Heuristics and Belief Tracking for Planning with Incomplete Information

Conformant planning can be formulated as a path-finding problem in belief space where the two main challenges are the heuristics to guide the search, and the representation and update of beliefs. In the translation-based approach recently introduced by Palacios and Geffner, the two aspects are handled together by translating conformant problems into classical ones that are solved with classical planners. While competitive with state-of-the-art methods, the translation-based approach runs however into three difficulties. First, complete translations are expensive for problems with high width; second, incomplete translations can generate infinite heuristic values for problems that are solvable; and third, aspects that are specific to the conformant setting, such as the cardinality of beliefs, are not accounted for.In this work, we build on the translation-based approach but not for solving conformant problems with a classical planner but for deriving heuristics and computing beliefs in the context of a standard belief-space planner. For this, a novel translation KSi is introduced that is always complete, but which is sound for problems with width bounded by i. A new conformant planner, called T1, builds then on this translation for i=1, extending the heuristic that results with a second heuristic obtained from invariant "oneof expressions". A number of experiments is performed to compare T1 with state-of-the-art conformant planners.

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

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.

PHASE TRANSITIONS OF EXPSPACE-COMPLETE PROBLEMS

This paper explores the phase transitions of the EXPSPACE-complete problems, which mainly focus on the conformant planning problems. The research presents two conformant planning algorithms—CONFORMANT PLAN-NONEXISTENCE algorithm and CONFORMANT PLAN-EXISTENCE algorithm. By analyzing the features of the two algorithms, the phase transition area of the conformant planning problems is obtained. If the number of the operators isn't greater than θub, the CONFORMANT PLAN-NONEXISTENCE algorithm can prove that nearly all the conformant planning instances have no solution. If the number of the operators isn't lower than θlb, the CONFORMANT PLAN-EXISTENCE algorithm can prove that nearly all the conformant planning instances have solutions. The results of the experiments show that there exist phase transitions from a region where almost all the conformant planning instances have no solution to a region where almost all the conformant planning instances have solutions.

On the Use of Prime Implicates in Conformant Planning

The paper presents an investigation of the use of two alternative forms of CNF formulae—prime implicates and minimal CNF—to compactly represent belief states in the context of conformant planning. For each representation, we define a transition function for computing the successor belief state resulting from the execution of an action in a belief state; results concerning soundness and completeness are provided. The paper describes a system (PIP) which dynamically selects either of these two forms to represent belief states, and an experimental evaluation of PIP against state-of-the-art conformant planners. The results show that PIP has the potential of scaling up better than other planners in problems rich in disjunctive information about the initial state.