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Автор	Тема: На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:34 PART-7: PLANNING and SCHEDULING: =288=> 1728p Cost-Optimal Planning with Landmarks, Erez Karpas, Carmel Domshlak, http://ijcai.org/papers09/Abstracts/288.html Planning landmarks are facts that must be true at some point in every solution plan. Previous work has very successfully exploited planning landmarks in satisficing (non-optimal) planning. We propose a methodology for deriving admissible heuristic estimates for cost-optimal planning from a set of planning landmarks. The resulting heuristics fall into a novel class of multi-path dependent heuristics, and we present a simple best-first search procedure exploiting such heuristics. Our empirical evaluation shows that this framework favorably competes with the state-of-the-art of cost-optimal heuristic search. text: http://ijcai.org/papers09/Papers/IJCAI09-288.pdf =============================== PART-7: PLANNING and SCHEDULING: =289=> 1734p Trees of Shortest Paths Versus Steiner Trees: Understanding and Improving Delete Relaxation Heuristics, Emil Ragip Keyder, Hector Geffner, http://ijcai.org/papers09/Abstracts/289.html Heuristic search using heuristics extracted from the delete relaxation is one of the most effective methods in planning. Since finding the optimal solution of the delete relaxation is intractable, various heuristics introduce independence assumptions, the implications of which are not yet fully understood. Here we use concepts from graph theory to show that in problems with unary action preconditions, the delete relaxation is closely related to the Steiner Tree problem, and that the independence assumption for the set of goals results in a tree-of-shortest-paths approximation. We analyze the limitations of this approximation and develop an alternative method for computing relaxed plans that addresses them. The method is used to guide a greedy best-first search, where it is shown to improve plan quality and coverage over several benchmark domains. text: http://ijcai.org/papers09/Papers/IJCAI09-289.pdf =============================== PART-7: PLANNING and SCHEDULING: =290=> 1740p Efficient Abstraction and Refinement for Behavioral Description Based Web Service Composition, Hyunyoung Kil, Wonhong Nam, Dongwon Lee, http://ijcai.org/papers09/Abstracts/290.html The Web Service Composition (WSC) problem with respect to behavioral descriptions deals with the automatic synthesis of a coordinator web service, c, that controls a set of web services to reach a goal state. Despite its importance, however, solving the WSC problem for a general case (when c has only partial observations) remains to be doubly exponential in the number of variables in web service descriptions, rendering any attempts to compute an exact solution for modest size impractical. Toward this challenge, in this paper, we propose two novel (signature preserving and subsuming) approximation-based approaches using abstraction and refinement. We empirically validate that our proposals can solve realistic problems efficiently. text: http://ijcai.org/papers09/Papers/IJCAI09-290.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:35 PART-7: PLANNING and SCHEDULING: =291=> 1746p ReTrASE: Integrating Paradigms for Approximate Probabilistic Planning, Andrey Kolobov, Mausam, Daniel S. Weld, http://ijcai.org/papers09/Abstracts/291.html Past approaches for solving MDPs have several weaknesses: 1) Decision-theoretic computation over the state space can yield optimal results but scales poorly. 2) Value-function approximation typically requires human-specified basis functions and has not been shown successful on nominal ("discrete") domains such as those in the ICAPS planning competitions. 3) Replanning by applying a classical planner to a determinized domain model can generate approximate policies for very large problems but has trouble handling probabilistic subtlety. This paper presents ReTrASE, a novel MDP solver, which combines decision theory, function approximation and classical planning in a new way. ReTrASE uses classical planning to create basis functions for value-function approximation and applies expected-utility analysis to this compact space. Our algorithm is memory-efficient and fast (due to its compact, approximate representation), returns high-quality solutions (due to the decision-theoretic framework) and does not require additional knowledge from domain engineers (since we apply classical planning to automatically construct the basis functions). Experiments demonstrate that ReTrASE outperforms winners from the past three probabilistic-planning competitions on many hard problems. text: http://ijcai.org/papers09/Papers/IJCAI09-291.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:35 PART-7: PLANNING and SCHEDULING: =292=> 1754p Learning Probabilistic Hierarchical Task Networks to Capture User Preferences, Nan Li, Subbarao Kambhampati, Sungwook Yoon, http://ijcai.org/papers09/Abstracts/292.html While much work on learning in planning focused on learning domain physics (i.e., action models), and search control knowledge, little attention has been paid towards learning user preferences on desirable plans. Hierarchical task networks (HTN) are known to provide an effective way to encode user prescriptions about what constitute good plans. However, manual construction of these methods is complex and error prone. In this paper, we propose a novel approach to learning probabilistic hierarchical task networks that capture user preferences by examining user-produced plans given no prior information about the methods (in contrast, most prior work on learning within the HTN framework focused on learning “method preconditions”—i.e., domain physics—assuming that the structure of the methods is given as input). We will show that this problem has close parallels to the problem of probabilistic grammar induction, and describe how grammar induction methods can be adapted to learn task networks. We will empirically demonstrate the effectiveness of our approach by showing that task networks we learn are able to generate plans with a distribution close to the distribution of the userpreferred plans. text: http://ijcai.org/papers09/Papers/IJCAI09-292.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:35 PART-7: PLANNING and SCHEDULING: =293=> 1760p A Distributed Control Loop for Autonomous Recovery in a Multi-Agent Plan, Roberto Micalizio, http://ijcai.org/papers09/Abstracts/293.html This paper considers the execution of a Multi-Agent Plan in a partially observable environment, and faces the problem of recovering from action failures. The paper formalizes a local plan repair strategy, where each agent in the system is responsible for controlling (monitoring and diagnosing) the actions it executes, and for autonomously repairing its own plan when an action failure is detected. The paper describes also how to mitigate the impact of an action failure on the plans of other agents when the local recovery strategy fails. text: http://ijcai.org/papers09/Papers/IJCAI09-293.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:35 PART-7: PLANNING and SCHEDULING: =294=> 1766p Monte-Carlo Exploration for Deterministic Planning, Hootan Nakhost, Martin Müller, http://ijcai.org/papers09/Abstracts/294.html Search methods based on Monte-Carlo simulation have recently led to breakthrough performance improvements in difficult game-playing domains such as Go and General Game Playing. Monte-Carlo Random Walk (MRW) planning applies Monte-Carlo ideas to deterministic classical planning. In the forward chaining planner Arvand, Monte-Carlo random walks are used to explore the local neighborhood of a search state for action selection. In contrast to the stochastic local search approach used in the recent planner Identidem, random walks yield a larger and unbiased sample of the search neighborhood, and require state evaluations only at the endpoints of each walk. On IPC-4 competition problems, the performance of Arvand is competitive with state of the art systems. text: http://ijcai.org/papers09/Papers/IJCAI09-294.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:35 PART-7: PLANNING and SCHEDULING: =295=> 1772p Planning with Partial Preference Models, Tuan A. Nguyen, Minh B. Do, S. Kambhampati, B. Srivastava, http://ijcai.org/papers09/Abstracts/295.html In many real-world planning scenarios, the users are interested in optimizing multiple objectives (such as makespan and execution cost), but are unable to express their exact tradeoff between those objectives. When a planner encounters such partial preference models, rather than look for a single optimal plan, it needs to present the pareto set of plans and let the user choose from them. This idea of presenting the full pareto set is fraught with both computational and user-interface challenges. To make it practical, we propose the approach of finding a representative subset of the pareto set. We measure the quality of this representative set using the Integrated Convex Preference (ICP) model, originally developed in the OR community. We implement several heuristic approaches based on the Metric-LPG planner to find a good solution set according to this measure. We present empirical results demonstrating the promise of our approach. text: http://ijcai.org/papers09/Papers/IJCAI09-295.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:35 PART-7: PLANNING and SCHEDULING: =296=> 1778p Plan Recognition as Planning, Miquel Ramírez, H.Geffner, http://ijcai.org/papers09/Abstracts/296.html In this work we aim to narrow the gap between plan recognition and planning by exploiting the powerand generality of recent planning algorithms for recognizing the set G∗ of goals G that explain a sequenceof observations given a domain theory. After providing a crisp definition of this set, we showby means of a suitable problem transformation that a goal G belongs to G∗ if there is an action sequence π that is an optimal plan for both the goal G and the goal G extended with extra goals representing the observations. Exploiting this result, we show how the set G∗ can be computed exactly and approximately by minor modifications of existing optimal and suboptimal planning algorithms, and existing polynomial heuristics. Experiments over several domains show that the suboptimal planning algorithms and the polynomial heuristics provide good approximations of the optimal goal set G∗ whilescaling up as well as state-of-the-art planning algorithms and heuristics. text: http://ijcai.org/papers09/Papers/IJCAI09-296.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:36 PART-7: PLANNING and SCHEDULING: =297=> 1784p Bayesian Real-time Dynamic Programming, Scott Sanner, Robby Goetschalckx, Kurt Driessens, Guy Shani, http://ijcai.org/papers09/Abstracts/297.html Real-time dynamic programming (RTDP) solves Markov decision processes (MDPs) when the initial state is restricted, by focusing dynamic programming on the envelope of states reachable from an initial state set. RTDP often provides performance guarantees without visiting the entire state space. Building on RTDP, recent work has sought to improve its efficiency through various optimizations, including maintaining upper and lower bounds to both govern trial termination and prioritize state exploration. In this work, we take a Bayesian perspective on these upper and lower bounds and use a value of perfect information (VPI) analysis to govern trial termination and exploration in a novel algorithm we call VPI-RTDP. VPI-RTDP leads to an improvement over state-of-the-art RTDP methods, empirically yielding up to a three-fold reduction in the amount of time and number of visited states required to achieve comparable policy performance. text: http://ijcai.org/papers09/Papers/IJCAI09-297.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:36 PART-7: PLANNING and SCHEDULING: =298=> 1790p HTN Planning with Preferences, Shirin Sohrabi, Jorge A. Baier, Sheila A. McIlraith, http://ijcai.org/papers09/Abstracts/298.html In this paper we address the problem of generating preferred plans by combining the procedural control knowledge specified by Hierarchical Task Networks (HTNs) with rich user preferences. To this end, we extend the popular Planning Domain Definition Language, PDDL3, to support specification of simple and temporally extended preferences over HTN constructs. To compute preferred HTN plans, we propose a branch-and-bound algorithm, together with a set of heuristics that, leveraging HTN structure, measure progress towards satisfaction of preferences. Our preference-based planner, HTNPLAN-P, is implemented as an extension of the SHOP2 planner. We compared our planner with SGPLAN5 and HPLAN-P — the top performers in the 2006 International Planning Competition preference tracks. HTNPLAN-P generated plans that in all but a few cases equalled or exceeded the quality of plans returned by HPLAN-P and SGPLAN5. While our implementation builds on SHOP2, the language and techniques proposed here are relevant to a broad range of HTN planners. text: http://ijcai.org/papers09/Papers/IJCAI09-298.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:36 PART-7: PLANNING and SCHEDULING: =299=> 1798p A Context Driven Approach for Workflow Mining, Fusun Yaman, Tim Oates, Mark Burstein, http://ijcai.org/papers09/Abstracts/299.html Existing work on workflow mining ignores the dataflow aspect of the problem. This is not acceptable for service-oriented applications that use Web services with typed inputs and outputs. We propose a novel algorithm WIT (Workflow Inference from Traces) which identifies the context similarities of the observed actions based on the dataflow and uses model merging techniques to generalize the control flow and the dataflow simultaneously. We identify the class of workflows that WIT can learn correctly. We implemented WIT and tested it on a real world medical scheduling domain where WIT was able to find a good approximation of the target workflow. text: http://ijcai.org/papers09/Papers/IJCAI09-299.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:36 PART-7: PLANNING and SCHEDULING: =300=> 1804p Learning HTN Method Preconditions and Action Models from Partial Observations, Hankz Hankui Zhuo, Derek Hao Hu, Chad Hogg, Qiang Yang, Hector Munoz-Avila, http://ijcai.org/papers09/Abstracts/300.html To apply hierarchical task network (HTN) planning to real-world planning problems, one needs to encode the HTN schemata and action models beforehand. However, acquiring such domain knowledge is difficult and time-consuming because the HTN domain definition involves a significant knowledge-engineering effort. A system that can learn the HTN planning domain knowledge automatically would save time and allow HTN planning to be used in domains where such knowledge-engineering effort is not feasible. In this paper, we present a formal framework and algorithms to acquire HTN planning domain knowledge, by learning the preconditions and effects of actions and preconditions of methods. Our algorithm, HTN-learner, first builds constraints from given observed \emph{decomposition trees} to build action models and method preconditions. It then solves these constraints using a weighted MAX-SAT solver. The solution can be converted to action models and method preconditions. Unlike prior work on HTN learning, we do not depend on complete action models or state information. We test the algorithm on several domains, and show that our HTN-learner algorithm is both effective and efficient. text: http://ijcai.org/papers09/Papers/IJCAI09-300.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:36 PART-7: PLANNING and SCHEDULING [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:37 PART-7: PLANNING and SCHEDULING [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:37 [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:37 [Ответ][Цитата]
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