Author name cluster

J. Benton 0001

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

10 papers

1 author row

ICAPS Conference 2018 Conference Paper

CHAP-E: A Plan Execution Assistant for Pilots

J. Benton 0001
David E. Smith 0001
John Kaneshige
Leslie Keely
Thomas Stucky

Pilots have benefited from ever-increasing and evolving automation techniques for many decades. This automation has allowed pilots to handle increasingly complex aircraft with greater safety, precision, and reduced workload. Unfortunately, it can also lead to misunderstandings and loss of situational awareness. In the face of malfunctions or unexpected events, pilots sometimes have an unclear picture of the situation and what to do next or must find and follow written procedures that do not take into account all the details of the particular situation. Pilots may also incorrectly assume the mode or state of an automated system and fail to perform certain necessary actions that they assumed the automated system would handle. To help alleviate these issues, we introduce the Cockpit Hierarchical Activity Planning and Execution CHAP-E system. CHAP-E provides pilots with intuitive graphical guidance on what actions need to be performed and when they need to be performed based on the aircraft and automation state, and projection of this state into the future. This assists pilots in both nominal and off-nominal flight situations.

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ICAPS Conference 2012 Conference Paper

Anticipatory On-Line Planning

Ethan Burns
J. Benton 0001
Wheeler Ruml
Sung Wook Yoon
Minh Binh Do

We consider the problem of on-line continual planning, in whichadditional goals may arrive while plans for previous goals are stillexecuting and plan quality depends on how quickly goals are achieved. This is a challenging problem even in domains with deterministicactions. One common and straightforward approach is reactive planning, in which plans are synthesized when a new goal arrives. In this paper, we adapt the technique of hindsight optimization from on-line schedulingand probabilistic planning to create an anticipatory on-line planningalgorithm. Using an estimate of the goal arrival distribution, wesample possible futures and use a deterministic planner to estimate thevalue of taking possible actions at each time step. Results in twobenchmark domains based on unmanned aerial vehicle planning andmanufacturing suggest that an anticipatory approach yields a superiorplanner that is sensitive not only to which action should be executed, but when.

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SoCS Conference 2012 Conference Paper

Better Parameter-Free Anytime Search by Minimizing Time Between Solutions

Jordan Tyler Thayer
J. Benton 0001
Malte Helmert

This paper presents a new anytime search algorithm, anytime explicitestimation search (AEES). AEES is an anytime search algorithm which attempts to minimize the time between improvements to its incumbent solution by taking advantage of the differences between solution cost and length. We provide an argument that minimizing the time between solutions is ideal behavior for an anytime search algorithm and show that when actions have differing costs, many state-of-the-art search algorithms, including the search strategy of LAMA11 and anytime nonparametric A*, do not minimize the time between solutions. An empirical evaluation on seven domains shows that AEES often has boththe shortest time between incumbent solutions and the best solution in hand for a wide variety of cutoffs.

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ICAPS Conference 2012 Conference Paper

Temporal Planning with Preferences and Time-Dependent Continuous Costs

J. Benton 0001
Amanda Jane Coles
Andrew Coles

Temporal planning methods usually focus on the objective of minimizing makespan. Unfortunately, this misses a large class of planning problems where it is important to consider a wider variety of temporal and non-temporal preferences, making makespan lower-order concern. In this paper we consider modeling and reasoning with plan quality metrics that are not directly correlated with plan makespan, building on the planner POPF. We begin with the preferences defined in PDDL3, and present a mixed integer programming encoding to manage the the interaction between the hard temporal constraints for plan steps, and soft temporal constraints for preferences. To widen the support of metrics that can be expressed directly in PDDL, we then discuss an extension to soft-deadlines with continuous cost functions, avoiding the need to approximate these with several PDDL3 discrete-cost preferences. We demonstrate the success of our new planner on the benchmark temporal planning problems with preferences, showing that it is the state-of-the-art for such problems. We then analyze the benefits of reasoning with continuous (versus discretized) models of domains with continuous cost functions, showing the improvement in solution quality afforded through making the continuous cost function directly available to the planner.

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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.

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ICAPS Conference 2010 Conference Paper

G-Value Plateaus: A Challenge for Planning

J. Benton 0001
Kartik Talamadupula
Patrick Eyerich
Robert Mattmüller
Subbarao Kambhampati

While the string of successes found in using heuristic, best-first search methods have provided positive reinforcement for continuing work along these lines, fundamental problems arise when handling objectives whose value does not change with search operations. An extreme case of this occurs when handling the objective of generating a temporal plan with short makespan. Typically used heuristic search methods assume strictly positive edge costs for their guarantees on completeness and optimality, while the usual ``fattening'' and ``advance time'' steps of heuristic search for temporal planning have the potential of resulting in ``g-value plateaus''. In this paper we point out some underlying difficulties with using modern heuristic search methods when operating over g-value plateaus and discuss how the presence of these problems contributes to the poor performance of heuristic search planners. To further illustrate this, we show empirical results on recent benchmarks using a planner made with makespan optimization in mind.

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ICAPS Conference 2010 Conference Paper

Improving Determinization in Hindsight for On-line Probabilistic Planning

Sung Wook Yoon
Wheeler Ruml
J. Benton 0001
Minh Binh Do

Recently, "determinization in hindsight" has enjoyed surprising success in on-line probabilistic planning. This technique evaluates the actions available in the current state by using non-probabilistic planning in deterministic approximations of the original domain. Although the approach has proven itself effective in many challenging domains, it is computationally very expensive. In this paper, we present three significant improvements to help mitigate this expense. First, we use a method for detecting potentially useful actions, allowing us to avoid estimating the values of unnecessary ones. Second, we exploit determinism in the domain by reusing relevant plans rather than computing new ones. Third, we improve action evaluation by increasing the chance that at least one determin- istic plan reaches a goal. Taken together, these improvements allow determinization in hindsight to scale significantly better on large or mostly-deterministic problems.

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IROS Conference 2009 Conference Paper

Finding and exploiting goal opportunities in real-time during plan execution

Paul W. Schermerhorn
J. Benton 0001
Matthias Scheutz
Kartik Talamadupula
Subbarao Kambhampati

Autonomous robots that operate in real-world domains face multiple challenges that make planning and goal selection difficult. Not only must planning and execution occur in real time, newly acquired knowledge can invalidate previous plans, and goals and their utilities can change during plan execution. However, these events can also provide opportunities, if the architecture is designed to react appropriately. We present here an architecture that integrates the SapaReplan planner with the DIARC robot architecture, allowing the architecture to react dynamically to changes in the robot's goal structures.

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ICAPS Conference 2008 Conference Paper

An Online Learning Method for Improving Over-Subscription Planning

Sung Wook Yoon
J. Benton 0001
Subbarao Kambhampati

Despite the recent resurgence of interest in learning methods for planning, most such efforts are still focused exclusively on classical planning problems. In this work, we investigate the effectiveness of learning approaches for improving over-subscription planning, a problem that has received significant recent interest. Viewing over-subscription planning as a domain-independent optimization problem, we adapt the STAGE (Boyan and Moore 2000) approach to learn and improve the plan search. The key challenge in our study is how to automate the feature generation process. In our case, we developed and experimented with a relational feature set, based on Taxonomic syntax as well as a propositional feature set, based on ground-facts. The feature generation process and training data generation process are all automatic, making it a completely domain-independent optimization process that takes advantage of online learning. In empirical studies, our proposed approach improved upon the baseline planner for over-subscription planning on many of the benchmark problems.

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ICAPS Conference 2007 Conference Paper

A Hybrid Linear Programming and Relaxed Plan Heuristic for Partial Satisfaction Planning Problems

J. Benton 0001
Menkes van den Briel
Subbarao Kambhampati

The availability of informed (but inadmissible) planning heuristics has enabled the development of highly scalable planning systems. Due to this success, a body of work has grown around modifying these heuristics to handle extensions to classical planning. Most recently, there has been an interest in addressing partial satisfaction planning problems, but existing heuristics fail to address the complex interactions that occur in these problems between action and goal selection. In this paper we provide a unique admissible heuristic based on linear programming that we use to solve a relaxed version of the partial satisfaction planning problem. We incorporate this heuristic in conjunction with a lookahead strategy in a branch and bound algorithm to solve a class of over-subscribed planning problems.

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