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481
Using Classical Planners to Solve Nondeterministic Planning Problems
"... Researchers have developed a huge number of algorithms to solve classical planning problems. We provide a way to use these algorithms, unmodified, to generate strong-cyclic solutions in fully-observable nondeterministic planning domains. Our experiments show that when using our technique with FF and ..."
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Cited by 17 (0 self)
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and SGPlan (two well-known classical planners), its performance compares quite favorably to that of MBP, one of the best-known planners for nondeterministic planning problems.
Using Refinement Search To Unify And Synthesize Classical Planners
, 1996
"... Though domain-dependent planners can be more efficient than general-purpose planners, no flexible and straight-forward framework is available to generate them. The trend in implementing planners has been to write general-purpose planners through traditional human-intensive programming effort. This t ..."
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. This thesis presents a general method for automatically generating efficient domain-specific classical planners from formal specifications using a semi-automated software synthesis system called Kestrel Interactive Development System (KIDS, for short). To do this, a unifying theory for general
Directed Search for Generalized Plans Using Classical Planners
"... We consider the problem of finding generalized plans for situations where the number of objects may be unknown and unbounded during planning. The input is a domain specification, a goal condition, and a class of concrete problem instances first-order representation. Starting with an empty generalize ..."
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Cited by 6 (4 self)
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generalized plan, our overall approach is to incrementally increase the applicability of the plan by identifying a problem instance that it cannot solve, invoking a classical planner to solve that problem, generalizing the obtained solution and merging it back into the generalized plan. The main contributions
Biasing the Structure of Scheduling Problems Through Classical Planners
- In Proceedings of the Workshop on Integrating Planning into Scheduling (WIPIS) at ICAPS
, 2004
"... Since Planning and Scheduling address complementary aspects of Problem Solving, much attention is recently being paid to the possibility of mutually exchanging the information yielded during the planning and the scheduling search procedures. In this context, we investigate a loosely coupled approach ..."
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Cited by 8 (6 self)
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approach, which consists of cascading a planner and a scheduler. While other implementations of this framework have already been reported, our work aims at analyzing the structural properties of the scheduling problem which results from the planning component, focusing on the bias produced by different
Using Classical Planners for Tasks with Continuous Operators in Robotics
"... The need for high-level task planning in robotics is well understood. However, interfacing discrete planning with continuous actions often requires extensive engineering of the solution. For instance, picking up an object may require removing many others that obstruct it. Identifying the exact obstr ..."
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Cited by 3 (0 self)
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obstructions requires geometric reasoning which is prohibitively expensive to precompute, with results that are difficult to represent efficiently at the level of a discrete planner. We propose a new approach that utilizes representation techniques from first-order logic and provides a method for synchronizing
Probabilistic plan recognition using off-the-shelf classical planners
- In Proc. AAAI-2010
"... Plan recognition is the problem of inferring the goals and plans of an agent after observing its behavior. Recently, it has been shown that this problem can be solved efficiently, without the need of a plan library, using slightly modified planning algorithms. In this work, we extend this approach t ..."
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Cited by 37 (5 self)
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efficiently using classical planners provided that the probability of a partially observed execution given a goal is defined in terms of the cost difference of achieving the goal under two conditions: complying with the observations, and not complying with them. This cost, and hence the posterior goal
Non-classical Planning with a Classical Planner: The Power of Transformations
"... Abstract. Planning is the model-based approach to autonomous behav-ior where a predictive model of actions and sensors is used to generate the behavior for achieving given goals. The main challenges in plan-ning are computational as all models, whether featuring uncertainty and feedback or not, are ..."
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, are intractable in the worst case when represented in compact form. Classical planning refers to the simplest form of plan-ning where goals are to be achieved by applying deterministic actions to a fully known initial situation. In this invited paper, I review the infer-ences performed by classical planners
Automatic Derivation of Memoryless Policies and Finite-State Controllers Using Classical Planners
"... (a) A B Finite-state and memoryless controllers are simple action selection mechanisms widely used in domains such as videogames and mobile robotics. Memoryless controllers stand for functions that map observations into actions, while finitestate controllers generalize memoryless ones with a finite ..."
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Cited by 36 (11 self)
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are observable. The problem of deriving a controller from such models is converted into a conformant planning problem that is solved using classical planners, taking advantage of a complete translation introduced recently. The controllers derived in this way are ‘general ’ in the sense that they do not solve
A New Approach to Conformant Planning via Classical Planners
"... In this paper, we introduce a new approach to conformant planning via classical planners. We view a conformant planning problem as a set of classical planning problems, called sub-problems, and solve it using a generate-and-complete algorithm. Key to this algorithm is a procedure which takes a solut ..."
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In this paper, we introduce a new approach to conformant planning via classical planners. We view a conformant planning problem as a set of classical planning problems, called sub-problems, and solve it using a generate-and-complete algorithm. Key to this algorithm is a procedure which takes a
Computing Infinite Plans for LTL Goals Using a Classical Planner
- PROCEEDINGS OF THE TWENTY-SECOND INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE
"... Classical planning has been notably successful in synthesizing finite plans to achieve states where propositional goals hold. In the last few years, classical planning has also been extended to incorporate temporally extended goals, expressed in temporal logics such as LTL, to impose restrictions on ..."
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Cited by 7 (4 self)
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on the state sequences generated by finite plans. In this work, we take the next step and consider the computation of infinite plans for achieving arbitrary LTL goals. We show that infinite plans can also be obtained efficiently by calling a classical planner once over a classical planning encoding
Results 1 - 10
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481
