Computing Contingent Plans Using Online Replanning (original) (raw)

Partially Observable Online Contingent Planning Using Landmark Heuristics

Twenty Fourth International Conference on Automated Planning and Scheduling, 2014

In contingent planning problems, agents have partial information about their state and use 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 the next 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.

Comparative criteria for partially observable contingent planning

Autonomous Agents and Multi-Agent Systems, 2019

In contingent planning under partial observability with sensing actions, the solution can be represented as a plan tree, branching on various possible observations. Typically, one seeks a satisfying plan leading to a goal state at each leaf. In many applications, however, one may prefer some satisfying plans to others. We focus on the problem of providing valid comparative criteria for contingent plan trees and graphs, allowing us to compare two plans and decide which one is preferable. We suggest a set of such comparison criteria-plan simplicity, dominance, and best and worst plan costs. In some cases certain branches of the plan correspond to an unlikely combination of mishaps, and can be ignored, and we provide methods for pruning such unlikely branches before comparing the plan graphs. We explain these criteria, and discuss their validity, correlations, and application to real world problems. We suggest efficient algorithms for computing the comparative criteria. We provide experimental results, showing that plans computed by existing contingent planners can be compared using the suggested criteria.

A Multi-Path Compilation Approach to Contingent Planning

2012

We describe a new sound and complete method for compiling contingent planning problems with sensing actions into classical planning. Our method encodes conditional plans within a linear, classical plan. This allows our planner, MPSR, to reason about multiple future outcomes of sensing actions, and makes it less susceptible to dead-ends. MPRS, however, generates very large classical planning problems. To overcome this, we use an incomplete variant of the method, based on state sampling, within an online replanner. On most current domains, MPSR finds plans faster, although its plans are often longer. But on a new challenging variant of Wumpus with dead-ends, it finds smaller plans, faster, and scales better.

Landmark-based heuristic online contingent planning

Autonomous Agents and Multi-Agent Systems, 2018

In contingent planning problems, agents have partial information about their state and use 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. We propose a heuristic, online method for contingent planning which focuses on identifying the next useful sensing action. We select the next sensing action based on a landmark heuristic, adapted from classical planning. We discuss landmarks for plan trees, providing several alternative definitions and discussing their merits. The key part of our planner is the novel landmarks-based heuristic, together with a projection method that uses classical planning to solve the intermediate conformant planning problems. The resulting heuristic contingent planner solves many more problems than state-of-the-art, translatio...

Planning with State Uncertainty via Contingency Planning and Execution Monitoring

This paper proposes a fast alternative to POMDP planning for domains with deterministic state-changing actions but probabilistic observation-making actions and partial observability of the initial state. This class of planning problems, which we call quasi-deterministic problems, includes important realworld domains such as planning for Mars rovers.The approach we take is to reformulate the quasi-deterministic problem into a completely observable problem and build a contingency plan where branches in the plan occur wherever an observational action is used to determine the value of some state variable. The plan for the completely observable problem is constructed assuming that state variables can be determined exactly at execution time using these observational actions. Since this is often not the case due to imperfect sensing of the world, we then use execution monitoring to select additional actions at execution time to determine the value of the state variable sufficiently accurately. We use a value of information calculation to determine which information gathering actions to perform and when to stop gathering information and continue execution of the branching plan. We show empirically that while the plans found are not optimal, they can be generated much faster, and are of better quality than other approaches.

A Switching Planner for Combined Task and Observation Planning

From an automated planning perspective the problem of practical mobile robot control in realistic environments poses many important and contrary challenges. On the one hand, the planning process must be lightweight, robust, and timely. Over the lifetime of the robot it must always respond quickly with new plans that accommodate exogenous events, changing objectives, and the underlying unpredictability of the environment. On the other hand, in order to promote efficient behaviours the planning process must perform computationally expensive reasoning about contingencies and possible revisions of subjective beliefs according to quantitatively modelled uncertainty in acting and sensing. Towards addressing these challenges, we develop a continual planning approach that switches between using a fast satisficing "classical" planner, to decide on the overall strategy, and decision-theoretic planning to solve small abstract subproblems where deeper consideration of the sensing model is both practical, and can significantly impact overall performance. We evaluate our approach in large problems from a realistic robot exploration domain.

Stubborn Sets for Fully Observable Nondeterministic Planning

2017

The stubborn set method is a state-space reduction technique, originally introduced in model checking and then transfered to classical planning. It was shown that stubborn sets significantly improve the performance of optimal deterministic planners by considering only a subset of applicable operators in a state. Fully observable nondeterministic planning (FOND) extends the formalism of classical planning by nondeterministic operators. We show that stubborn sets are also beneficial for FOND problems. We introduce nondeterministic stubborn sets, stubborn sets which preserve strong cyclic plans. We follow two approaches: Fast Incremental Planning with stubborn sets from classical planning and LAO* search with nondeterministic stubborn sets. Our experiments show that both approaches increase coverage and decrease node generations when compared to their respective baselines.

A Switching Planner for Combined Task and Observation Planning (workshop version)

From an automated planning perspective the problem of practical mobile robot control in realistic environments poses many important and contrary challenges. On the one hand, the planning process must be lightweight, robust, and timely. Over the lifetime of the robot it must always respond quickly with new plans that accommodate exogenous events, changing objectives, and the underlying unpredictability of the environment. On the other hand, in order to promote efficient behaviours the planning process must perform computationally expensive reasoning about contingencies and possible revisions of subjective beliefs according to quantitatively modelled uncertainty in acting and sensing. Towards addressing these challenges, we develop a continual planning approach that switches between using a fast satisficing "classical" planner, to decide on the overall strategy, and decision-theoretic planning to solve small abstract subproblems where deeper consideration of the sensing model is both practical, and can significantly impact overall performance. We evaluate our approach in large problems from a realistic robot exploration domain.

Interleaving execution and planning for nondeterministic, partially observable domains

2004

Methods that interleave planning and execution are a practical solution to deal with complex planning problems in nondeterministic domains under partial observability. However, most of the existing approaches do not tackle in a principled way the important issue of termination of the planning-execution loop, or only do so considering specific assumptions over the domains.