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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 5:29 PART 7: PLANNING and SCHEDULING 1622p [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:32 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 5:32 PART-7: PLANNING and SCHEDULING: =271=> 1623p A Translation-based Approach to Contingent Planning, Alexandre Albore, Héctor Palacios, Héctor Geffner, http://ijcai.org/papers09/Abstracts/271.html The problem of planning in the presence of sensing has been addressed in recent years as a non-deterministic search problem in belief space. In this work, we use ideas advanced recently for compiling conformant problems into classical ones for introducing a different approach where contingent problems P are mapped into non-deterministic problems X(P) in state space. We also identify a contingent width parameter, and show that for problems P with bounded contingent width, the translation is sound, polynomial, and complete. We then solve X(P) by using a relaxation X+(P) that is a classical planning problem. The formulation is tested experimentally over contingent benchmarks where it is shown to yield a planner that scales up better than existing contingent planners. text: http://ijcai.org/papers09/Papers/IJCAI09-271.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 5:32 PART-7: PLANNING and SCHEDULING: =272=> 1629p Translating HTNs to PDDL: a Small Amount of Domain Knowledge Can Go a Long Way, Ronald Wayne Alford, Ugur Kuter, Dana Nau, http://ijcai.org/papers09/Abstracts/272.html We show how to translate HTN domain descriptions (if they satisfy certain restrictions) into PDDL so that they can be used by classical planners. We provide correctness results for our translation algorithm, and show that it runs in linear time and space. We also show that even small and incomplete amounts of HTN knowledge, when translated into PDDL using our algorithm, can greatly improve a classical planner's performance. In experiments on several thousand randomly generated problems in three different planning domains, such knowledge speeded up the well-known Fast-Forward planner by several orders of magnitude, and enabled it to solve much larger problems than it could otherwise solve. text: http://ijcai.org/papers09/Papers/IJCAI09-272.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 5:32 PART-7: PLANNING and SCHEDULING: =273=> 1635p Goal Recognition with Variable-Order Markov Models, Marcelo Gabriel Armentano, AA Amandi, http://ijcai.org/papers09/Abstracts/273.html The recognition of the goal a user is pursing when interacting with a software application is a crucial task for an interface agent as it serves as a context for making opportune interventions to provide assistance to the user. The prediction of the user goal must be fast and a goal recognizer must be able to make early predictions with few observations of the user actions. In this work we propose an approach to automatically build an intention model from a plan corpus using Variable Order Markov models. We claim that following our approach, an interface agent will be capable of accurately ranking the most probable user goals in a time linear to the number of goals modeled. text: http://ijcai.org/papers09/Papers/IJCAI09-273.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 5:33 PART-7: PLANNING and SCHEDULING: =274=> 1641p Solving POMDPs: RTDP-Bel Versus Point-based Algorithms, Blai Bonet, Hector Geffner, http://ijcai.org/papers09/Abstracts/274.html Point-based algorithms and RTDP-Bel are approximate methods for solving POMDPs that replace the full updates of parallel value iteration by faster and more effective updates at selected beliefs. An important difference between the two methods is that the former adopt Sondik's representation of the value function, while the latter uses a tabular representation and a discretization function. The algorithms, however, have not been compared up to now, because they target different POMDPs: discounted POMDPs on the one hand, and Goal POMDPs on the other. In this paper, we bridge this representational gap, showing how to transform discounted POMDPs into Goal POMDPs, and use the transformation to compare RTDP-Bel with point-based algorithms over the existing discounted benchmarks. The results appear to contradict the conventional wisdom in the area showing that RTDP-Bel is competitive, and sometimes superior to point-based algorithms in both quality and time. text: http://ijcai.org/papers09/Papers/IJCAI09-274.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 5:33 PART-7: PLANNING and SCHEDULING: =275=> 1647p Incremental Heuristic Search for Planning with Temporally Extended Goals and Uncontrollable Events, Adi Botea, Andre A. Cire, http://ijcai.org/papers09/Abstracts/275.html Planning with temporally extended goals and uncontrollable events has recently been introduced as a formal model for system reconfiguration problems. An important application is to automatically reconfigure a real-life system in such a way that its subsequent internal evolution is consistent with a temporal goal formula. In this paper we introduce an incremental search algorithm and a search-guidance heuristic, two generic planning enhancements. An initial problem is decomposed into a series of subproblems, providing two main ways of speeding up a search. Firstly, a subproblem focuses on a part of the initial goal. Secondly, a notion of action relevance allows to explore with higher priority actions that are heuristically considered to be more relevant to the subproblem at hand. Even though our techniques are more generally applicable, we restrict our attention to planning with temporally extended goals and uncontrollable events. Our ideas are implemented on top of a successful previous system that performs online learning to better guide planning and to safely avoid potentially expensive searches. In experiments, the system speed performance is further improved by a convincing margin. text: http://ijcai.org/papers09/Papers/IJCAI09-275.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 5:33 PART-7: PLANNING and SCHEDULING: =276=> 1653p Equivalence Relations in Fully and Partially Observable Markov Decision Processes, Pablo Samuel Castro, Prakash Panangaden, Doina Precup, http://ijcai.org/papers09/Abstracts/276.html We explore equivalence relations between states in Markov Decision Processes and Partially Observable Markov Decision Processes. We focus on two different equivalence notions: bisimulation (Givan et al., 2003) and a notion of trace equivalence, under which states are considered equivalent if they generate the same conditional probability distributions over observation sequences (where the conditioning is on action sequences). We show that the relationship between these two equivalence notions changes depending on the amount and nature of the partial observability. We also present an alternate characterization of bisimulation based on trajectory equivalence. text: http://ijcai.org/papers09/Papers/IJCAI09-276.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 5:33 PART-7: PLANNING and SCHEDULING: =277=> 1659p Completeness and Optimality Preserving Reduction for Planning, Yixin Chen, Guohui Yao, http://ijcai.org/papers09/Abstracts/277.html Traditional AI search methods search in a state space typically modelled as a directed graph. Prohibitively large sizes of state space graphs make complete or optimal search expensive. A key observation, as exemplified by the SAS+ formalism for planning, is that most commonly a state-space graph can be decomposed into subgraphs, linked by constraints. We propose a novel space reduction algorithm that exploits such structure. The result reveals that standard search algorithms may explore many redundant paths. Our method provides an automatic way to remove such redundancy. At each state, we expand only the subgraphs within a dependency closure satisfying certain sufficient conditions instead of all the subgraphs. Theoretically we prove that the proposed algorithm is completeness-preserving as well as optimality-preserving. We show that our reduction method can significantly reduce the search cost on a collection of planning domains. text: http://ijcai.org/papers09/Papers/IJCAI09-277.pdf =============================== PART-7: PLANNING and SCHEDULING: =278=> 1665p Stratified Planning, Yixin Chen, You Xu, Guohui Yao, http://ijcai.org/papers09/Abstracts/278.html Most planning problems have strong structures. They can be decomposed into subdomains with causal dependencies. The idea of exploiting the domain decomposition has motivated previous work such as hierarchical planning and factored planing. However, these algorithms require extensive backtracking and lead to few efficient general-purpose planners. On the other hand, heuristic search has been a successful approach to automated planning. The domain decomposition of planning problems, unfortunately, is not directly and fully exploited by heuristic search. We propose a novel and general framework to exploit domain decomposition. Based on a structure analysis on the SAS+ planning formalism, we stratify the sub-domains of a planning problem into dependency layers. By recognizing the stratification of a planning structure, we propose a space reduction method that expands only a subset of executable actions at each state. This reduction method can be combined with state-space search, allowing us to simultaneously employ the strength of domain decomposition and high-quality heuristics. We prove that the reduction preserves completeness and optimality of search and experimentally verify its effectiveness in space reduction. text: http://ijcai.org/papers09/Papers/IJCAI09-278.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 5:33 PART-7: PLANNING and SCHEDULING: =279=> 1671p Temporal Planning in Domains with Linear Processes, Amanda Coles, Andrew Coles, Maria Fox, Derek Long, http://ijcai.org/papers09/Abstracts/279.html We consider the problem of planning in domains with continuous linear numeric change. Such change cannot always be adequately modelled by discretisation and is a key facet of many interesting problems. We show how a forward-chaining temporal planner can be extended to reason with actions with continuous linear effects. We extend a temporal planner to handle numeric values using linear programming. We show how linear continuous change can be integrated into the same linear program and we discuss how a temporal-numeric heuristic can be used to provide the search guidance necessary to underpin continuous planning. We present results to show that the approach can effectively handle duration-dependent change and numeric variables subject to continuous linear change. text: http://ijcai.org/papers09/Papers/IJCAI09-279.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 5:33 PART-7: PLANNING and SCHEDULING: =280=> 1677p Domain-Independent, Automatic Partitioning for Probabilistic Planning, Peng Dai, Mausam, Daniel S Weld, http://ijcai.org/papers09/Abstracts/280.html Recent progress on external-memory MDP solvers has enabled optimal solutions to large probabilistic planning problems. However, PEMVI requires a human to manually partition the MDP before the planning algorithm can be applied — putting an added burden on the domain designer and detracting from the vision of automated planning. This paper presents a novel partitioning scheme, which automatically subdivides the state space into blocks that respect the memory constraints. Our algorithm first applies static domain analysis to identify candidates for partitioning, and then uses heuristic search to generate a good partition. We evaluate the usefulness of our method in the context of PEMVI across many benchmark domains, showing that it can successfully solve extremely large problems in each domain. We also compare the performance of automatic partitioning with previously reported results using human-designed partitions. Experiments show that our algorithm generates significantly superior partitions, which speed MDP solving and also yield vast memory savings. text: http://ijcai.org/papers09/Papers/IJCAI09-280.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 5:34 PART-7: PLANNING and SCHEDULING: =281=> 1684p Topological Order Planner for POMDPs, Jilles Steeve Dibangoye, Guy Shani, Brahim Chaib-draa, Abdell-Illah Mouaddib, http://ijcai.org/papers09/Abstracts/281.html Over the past few years, point-based POMDP solvers scaled up to produce approximate solutions to mid-sized domains. However, to solve real world problems, solvers must exploit the structure of the domain. In this paper we focus on the topological structure of the problem, where the state space contains layers of states. We present here the Topological Order Planner (TOP) that utilizes the topological structure of the domain to compute belief space trajectories. TOP rapidly produces trajectories focused on the solveable regions of the belief space, thus reducing the number of redundant backups considerably. We demonstrate TOP to produce good quality policies faster than any other point-based algorithm on domains with sufficient structure. text: http://ijcai.org/papers09/Papers/IJCAI09-281.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 5:34 PART-7: PLANNING and SCHEDULING: =282=> 1690p Optimal Symbolic Planning with Action Costs & Preferences, Stefan Edelkamp, P. Kissmann, http://ijcai.org/papers09/Abstracts/282.html This paper studies the solving of finite-domain action planning problems with discrete action costs and soft constraints. For sequential optimal planning, a symbolic perimeter database heuristic is addressed in a bucket implementation of A*. For computing net-benefits, we propose symbolic branch-and-bound search together with some search refinements. The net-benefit we optimize is the total benefit of satisfying the goals, minus the total action cost to achieve them. This results in an objective function to be minimized that is a linear expression over the violation of the preferences added to the action cost total. text: http://ijcai.org/papers09/Papers/IJCAI09-282.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 5:34 PART-7: PLANNING and SCHEDULING: =283=> 1696p Activity Recognition with Intended Actions, A.Gabaldon, http://ijcai.org/papers09/Abstracts/283.html The following activity recognition problem is considered: a description of the action capabilities of an agent being observed is given. This includes the preconditions and effects of atomic actions and of the activities (sequences of actions) the agent may execute. Given this description and a set of propositions, called history, about action occurrences, intended actions and properties of the world all at various points in time, the problem is to complete the picture as much as possible and determine what has already happened, what the intentions of the agent are, and what may happen as a result of the agent acting on those intentions. We present a framework to solve these activity recognition problems based on a formal language for reasoning about actions that includes a notion of intended actions, and a corresponding formalization in answer set programming. text: http://ijcai.org/papers09/Papers/IJCAI09-283.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 5:34 PART-7: PLANNING and SCHEDULING: =284=> 1702p Delaying Commitment in Plan Recognition Using Combinatory Categorial Grammars, Christopher W. Geib, http://ijcai.org/papers09/Abstracts/284.html This paper presents a new algorithm for plan recognition called ELEXIR (Engine for LEXicalized Intent Recognition). ELEXIR represents the plans to be recognized with a grammatical formalism called Combinatory Categorial Grammar (CCG). We show that representing plans with CCGs can allow us to prevent early commitment to plan goals and thereby reduce runtime. text: http://ijcai.org/papers09/Papers/IJCAI09-284.pdf =============================== PART-7: PLANNING and SCHEDULING: =285=> 1708p Learning Hierarchical Task Networks for Nondeterministic Planning Domains, Chad Hogg, Ugur Kuter, Hector Munoz-Avila, http://ijcai.org/papers09/Abstracts/285.html This paper describes how to learn Hierarchical Task Networks (HTNs) in nondeterministic planning domains, where actions may have multiple possible outcomes. We discuss several desired properties that guarantee that the resulting HTNs will correctly handle the nondeterminism in the domain. We developed a new learning algorithm, called ND-HTN-Maker, that exploits these properties. We implemented ND-HTN-Maker in the recently-proposed HTN-Maker system, a goal-regression based HTN learning approach. In our theoretical study, we show that ND-HTN-Maker soundly produces HTN planning knowledge in low-order polynomial times, despite the nondeterminism. In our experiments with two nondeterministic planning domains, ND-SHOP2, a well-known HTN planning algorithm for nondeterministic domains, significantly outperformed (in some cases, by about 3 orders of magnitude) the well-known planner MBP using the learned HTNs. text: http://ijcai.org/papers09/Papers/IJCAI09-285.pdf =============================== PART-7: PLANNING and SCHEDULING: =286=> 1715p Abnormal Activity Recognition based on HDP-HMM Models, Derek Hao Hu, Xian-Xing Zhang, Jie Yin, Vincent Wenchen Zheng, Qiang Yang, http://ijcai.org/papers09/Abstracts/286.html Detecting abnormal activities from sensor readings is an important research problem in activity recognition. A number of different algorithms have been proposed in the past to tackle this problem. Many of the previous state-based approaches suffer from the problem of failing to decide the appropriate number of states, which are difficult to find through a trial and-error approach, in real-world applications. In this paper, we propose an accurate and flexible framework for abnormal activity recognition from sensor readings that involves less human tuning of model parameters. Our approach first applies a Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM), which supports an infinite number of states, to automatically find an appropriate number of states. We incorporate a Fisher Kernel into the One-Class Support Vector Machine (OCSVM) model to filter out the activities that are likely to be normal. Finally, we derive an abnormal activity model from the normal activity models to reduce false positive rate in an unsupervised manner. Our main contribution is that our proposed HDP-HMM models can decide the appropriate number of states automatically, and that by incorporating a Fisher Kernel into the OCSVM model, we can combine the advantages from generative model and discriminative model. We demonstrate the effectiveness of our approach by using several real-world datasets to test our algorithm’s performance. text: http://ijcai.org/papers09/Papers/IJCAI09-286.pdf =============================== PART-7: PLANNING and SCHEDULING: =287=> 1721p Structured Plans and Observation Reduction for Plans with Contexts, Wei Huang, Zhonghua Wen, Yunfei Jiang, Hong Peng, http://ijcai.org/papers09/Abstracts/287.html In many real world planning domains, some observation information is optional and useless to the execution of a plan; on the other hand, information acquisition may require some kind of cost. The problem of observation reduction for strong plans has been addressed in the literature. However, observation reduction for plans with contexts (which are more general and useful than strong plans in robotics) is still a open problem. In this paper, we present an attempt to solve the problem. Our first contribution is the definition of structured plans, which can encode sequential, conditional and iterative behaviors, and is expressive enough for dealing with incomplete observation information and internal states of the agent. A second contribution is an observation reduction algorithm for plans with contexts, which can transform a plan with contexts into a structured plan that only branches on necessary observation information. text: http://ijcai.org/papers09/Papers/IJCAI09-287.pdf [Ответ][Цитата]
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