liu.seSearch for publications in DiVA
Change search
Refine search result
1 - 18 of 18
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Doherty, Patrick
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Haslum, Patrik
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Heintz, Fredrik
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Merz, Torsten
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group .
    Nyblom, Per
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Persson, Tommy
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group .
    Wingman, Björn
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group .
    A Distributed Architecture for Autonomous Unmanned Aerial Vehicle Experimentation2004In: 7th International Symposium on Distributed Autonomous Robotic Systems,2004, Toulouse: LAAS , 2004, p. 221-Conference paper (Refereed)
  • 2.
    Haslum, Patrik
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Admissible Heuristics for Automated Planning2006Doctoral thesis, monograph (Other academic)
    Abstract [en]

    The problem of domain-independent automated planning has been a topic of research in Artificial Intelligence since the very beginnings of the field. Due to the desire not to rely on vast quantities of problem specific knowledge, the most widely adopted approach to automated planning is search. The topic of this thesis is the development of methods for achieving effective search control for domain-independent optimal planning through the construction of admissible heuristics. The particular planning problem considered is the so called “classical” AI planning problem, which makes several restricting assumptions. Optimality with respect to two measures of plan cost are considered: in planning with additive cost, the cost of a plan is the sum of the costs of the actions that make up the plan, which are assumed independent, while in planning with time, the cost of a plan is the total execution time – makespan – of the plan. The makespan optimization objective can not, in general, be formulated as a sum of independent action costs and therefore necessitates a problem model slightly different from the classical one. A further small extension to the classical model is made with the introduction of two forms of capacitated resources. Heuristics are developed mainly for regression planning, but based on principles general enough that heuristics for other planning search spaces can be derived on the same basis. The thesis describes a collection of methods, including the hm, additive hm and improved pattern database heuristics, and the relaxed search and boosting techniques for improving heuristics through limited search, and presents two extended experimental analyses of the developed methods, one comparing heuristics for planning with additive cost and the other concerning the relaxed search technique in the context of planning with time, aimed at discovering the characteristics of problem domains that determine the relative effectiveness of the compared methods. Results indicate that some plausible such characteristics have been found, but are not entirely conclusive.

    Download full text (pdf)
    FULLTEXT01
    Download (pdf)
    ERRATA01
  • 3.
    Haslum, Patrik
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Improving heuristics through relaxed search - An analysis of TP4 and HSP*a in the 2004 planning competition2006In: The journal of artificial intelligence research, ISSN 1076-9757, E-ISSN 1943-5037, Vol. 25, p. 233-267Article in journal (Refereed)
    Abstract [en]

    The h(m) admissible heuristics for (sequential and temporal) regression planning are defined by a parameterized relaxation of the optimal cost function in the regression search space, where the parameter m offers a trade-off between the accuracy and computational cost of the heuristic. Existing methods for computing the h(m) heuristic require time exponential in m, limiting them to small values (m <= 2). The h(m) heuristic can also be viewed as the optimal cost function in a relaxation of the search space: this paper presents relaxed search, a method for computing this function partially by searching in the relaxed space. The relaxed search method, because it compute h(m) only partially, is computationally cheaper and therefore usable for higher values of m. The (complete) h(2) heuristic is combined with partial hm heuristics , for m = 3, ... computed by relaxed search, resulting in a more accurate heuristic. This use of the relaxed search method to improve on the h(2) heuristic is evaluated by comparing two optimal temporal planners: TP4, which does not use it, and HSP*(a), which uses it but is otherwise identical to TP4. The comparison is made on the domains used in the 2004 International Planning Competition, in which both planners participated. Relaxed search is found to be cost effective in some of these domains, but not all. Analysis reveals a characterization of the domains in which relaxed search can be expected to be cost effective, in terms of two measures on the original and relaxed search spaces. In the domains where relaxed search is cost effective, expanding small states is computationally cheaper than expanding large states and small states tend to have small successor states.

  • 4.
    Haslum, Patrik
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Improving Heuristics Through Search2004In: Proceedings of the 16th Eureopean Conference on Artificial Intelligence (ECAI) / [ed] Ramon López de Mántaras, Lorenza Saitta, Amsterdam: IOS Press, 2004, p. 1031-1032Conference paper (Refereed)
    Abstract [en]

    We investigate two methods of using limited search to improve admissible heuristics for planning, similar to pattern databases and pattern searches. We also develop a new algorithm for searching AND/OR graphs

  • 5.
    Haslum, Patrik
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Model Checking by Random Walk1999In: Proceedings of the ECSEL Workshop (CCSSE), 1999Conference paper (Other academic)
    Abstract [en]

    While model checking algorithms are in theory efficient, they are in practice hampered by the explosive growth of system models. We show that for certain specifications the model cheking problem reduces to a question of reachability in the system state transition graph, and apply a simple, randomized algorithm to this problem.

  • 6.
    Haslum, Patrik
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Models for Prediction2001In: Proceedings of the IJCAI 2001 workshop on Planning under Uncertainty and Incomplete Information (PRO-2), 2001Conference paper (Refereed)
    Abstract [en]

    Prediction is found to be a part of many more complex reasoning problems, e.g. state estimation, planning and diagnosis. In spite of this, the prediction problem is rarely studied on its own. Yet there appears to be a wide range of choices for the design of the central component in a solution to this problem, the predictive model. We examine some of the alternatives and, as a case study, present two different solutions to a specific prediction problem that we have encountered in the WITAS UAV project.

  • 7.
    Haslum, Patrik
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Partial State Progression: An Extension to the Bacchus-Kabanza Algorithm, with Applications to Prediction and MITL Consistency2002In: Proceedings of the AIPS 2002 workshop on Planning via Model Checking, 2002Conference paper (Refereed)
  • 8.
    Haslum, Patrik
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Patterns in Reactive Programs2004In: Proceedings of the 4th International Cognitive Robotics Workshop (COGROB) / [ed] Patrick Doherty, Gerhard Lakemeyer, Angel P. de Pobil, 2004, p. 25-29Conference paper (Refereed)
    Abstract [en]

    In this paper, I explore the idea that there are “patterns”,analogous to software design patterns, in the kind of task proceduresthat frequently form the reactive component of architectures for intelligentautonomous systems. The investigation is carried out mainlywithin the context of the WITAS UAV project.

  • 9.
    Haslum, Patrik
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Prediction as a Knowledge Representation Problem: A Case Study in Model Design2002Licentiate thesis, monograph (Other academic)
    Abstract [en]

    The WITAS project aims to develop technologies to enable an Unmanned Airial Vehicle (UAV) to operate autonomously and intelligently, in applications such as traffic surveillance and remote photogrammetry. Many of the necessary control and reasoning tasks, e.g. state estimation, reidentification, planning and diagnosis, involve prediction as an important component. Prediction relies on models, and such models can take a variety of forms. Model design involves many choices with many alternatives for each choice, and each alternative carries advantages and disadvantages that may be far from obvious. In spite of this, and of the important role of prediction in so many areas, the problem of predictive model design is rarely studied on its own.

    In this thesis, we examine a range of applications involving prediction and try to extract a set of choices and alternatives for model design. As a case study, we then develop, evaluate and compare two different model designs for a specific prediction problem encountered in the WITAS UAV project. The problem is to predict the movements of a vehicle travelling in a traffic network. The main difficulty is that uncertainty in predictions is very high, du to two factors: predictions have to be made on a relatively large time scale, and we have very little information about the specific vehicle in question. To counter uncertainty, as much use as possible must be made of knowledge about traffic in general, which puts emphasis on the knowledge representation aspect of the predictive model design.

    The two mode design we develop differ mainly in how they represent uncertainty: the first uses coarse, schema-based representation of likelihood, while the second, a Markov model, uses probability. Preliminary experiments indicate that the second design has better computational properties, but also some drawbacks: model construction is data intensive and the resulting models are somewhat opaque.

    Download full text (pdf)
    FULLTEXT01
  • 10.
    Haslum, Patrik
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Bonet, Blai
    Departamento de Computación Universidad Simón Bolívar.
    Geffner, Hector
    Departamento de Tecnologia Universitat Pompeu Fabra.
    New Admissible Heuristics for Domain-Independent Planning2005In: Proceedings of the 20th national ´Conference on Artificial Intelligence (AAAI), AAAI Press , 2005, p. 1163-Conference paper (Refereed)
    Abstract [en]

    Admissible heuristics are critical for effective domain-independent planning when optimal solutions must be guaranteed. Two useful heuristics are the hm heuristics, which generalize the reachability heuristic underlying the planning graph, and pattern database heuristics. These heuristics, however, have serious limitations: reachability heuristics capture only the cost of critical paths in a relaxed problem, ignoring the cost of other relevant paths, while PDB heuristics, additive or not, cannot accommodate too many variables in patterns, and methods for automatically selecting patterns that produce good estimates are not known.

    We introduce two refinements of these heuristics: First, the additive hm heuristic which yields an admissible sum of hm heuristics using a partitioning of the set of actions. Second, the constrained PDB heuristic which uses constraints from the original problem to strengthen the lower bounds obtained from abstractions.

    The new heuristics depend on the way the actions or problem variables are partitioned. We advance methods for automatically deriving additive hm and PDB heuristics from STRIPS encodings. Evaluation shows improvement over existing heuristics in several domains, although, not surprisingly, no heuristic dominates all the others over all domains.

  • 11.
    Haslum, Patrik
    et al.
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Geffner, Héctor
    Universidad Simón Bolivar, Venezuela.
    Admissible Heuristics for Optimal Planning2000In: Proceedings of the 5th International Conference on Artificial Intelligence Planning and Scheduling (AIPS) / [ed] Steve Chien, Subbarao Kambhampati, Craig A. Knoblock, AAAI Press , 2000, p. 140-149Conference paper (Refereed)
    Abstract [en]

    hsp and hspr are two recent planners that search the state-space using an heuristic function extracted from Strips encodings. hsp does a forward search from the initial state recomputing the heuristic in every state, while hspr does a regression search from the goal computing a suitable representation of the heuristic only once. Both planners have shown good performance, often producing solutions that are competitive in time and number of actions with the solutions found by Graphplan and sat planners. hsp and hsp r, however, are not optimal planners. This is because the heuristic function is not admissible and the search algorithms are not optimal. In this paper we address this problem. We formulate a new admissible heuristic for planning, use it to guide an ida search, and empirically evaluate the resulting optimal planner over a number of domains. The main contribution is the idea underlying the heuristic that yields not one but a whole family of polynomial and admissible heuristics that trade accuracy for efficiency. The formulation is general and sheds some light on the heuristics used in hsp and Graphplan, and their relation. It exploits the factored (Strips) representation of planning problems, mapping shortest-path problems in state-space into suitably defined shortest-path problems in atom-space. The formulation applies with little variation to sequential and parallel planning, and problems with different action costs.

  • 12.
    Haslum, Patrik
    et al.
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Geffner, Héctor
    Universidad Simón Bolivar, Venezuela.
    Heuristic Planning with Time and Resources2001In: Proceedings of the 6th European Conference on Planning (ECP), 2001Conference paper (Refereed)
    Abstract [en]

    We present an algorithm for planning with time and resources based on heuristic search. The algorithm minimizes makespan using an admissible heuristic derived automatically from the problem instance. Estimators for resource consumption are derived in the same way. The goals are twofold: to show the flexibility of the heuristic search approach to planning and to develop a planner that combines expressivity and performance. Two main issues are the definition of regression in a temporal setting and the definition of the heuristic estimating completion time. A number of experiments are presented for assessing the performance of the resulting planner.

  • 13.
    Haslum, Patrik
    et al.
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Jonsson, Peter
    Linköping University, Department of Computer and Information Science, TCSLAB - Theoretical Computer Science Laboratory. Linköping University, The Institute of Technology.
    Planning with Reduced Operator Sets2000In: Proceedings of the 5th International Conference on Artificial Intelligence Planning and Scheduling (AIPS) / [ed] Steve Chien, Subbarao Kambhampati, Craig A. Knoblock, AAAI Press , 2000, p. 150-158Conference paper (Refereed)
    Abstract [en]

    Classical propositional STRIPS planning is nothing but the search for a path in the state transition graph induced by the operators in the planning problem. What makes the problem hard is the size and the sometimes adverse structure of this graph. We conjecture that the search for a plan would be more efficient if there were only a small number of paths from the initial state to the goal state. To verify this conjecture, we define the notion of reduced operator sets and describe ways of finding such reduced sets. We demonstrate that some state-of-the-art planners run faster using reduced operator sets.

  • 14.
    Haslum, Patrik
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Jonsson, Peter
    Some results on the complexity of planning with incomplete information1999In: Proceedings of the 5th European Conference on Planning (ECP), Springer , 1999, Vol. 1809, p. 308-318Conference paper (Refereed)
    Abstract [en]

    Planning with incomplete information may mean a number of different things, that certain facts of the initial state are not known, that operators can have random or nondeterministic effects, or that the plans created contain sensing operations and are branching. Study of the complexity of incomplete information planning has so far been concentrated on probabilistic domains, where a number of results have been found. We examine the complexity of planning in nondeterministic propositional domains. This differs from domains involving randomness, which has been well studied, in that for a nondeterministic choice, not even a probability distribution over the possible outcomes is known. The main result of this paper is that the non-branching plan existence problem in unobservable domains with an expressive operator formalism is EXPSPACE-complete. We also discuss several restrictions, which bring the complexity of the problem down to PSPACF-complete, and extensions to the fully and partially observable cases.

  • 15.
    Haslum, Patrik
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Scholz, Ulrich
    Fachbereich Informatik Technische Universität Darmstadt.
    Domain Knowledge in Planning: Representation and Use2003In: Proceedings of the ICAPS workshop on PDDL, 2003, p. 69-78Conference paper (Refereed)
    Abstract [en]

    Planning systems rely on knowledge about the problems they have to solve: The problem description and in many cases advice on how to find a solution. This paper is concerned with a third kind of knowledge which we term domain knowledge: Information about the problem that is produced by one component of the planner and used for advice by another. We first distinguish domain knowledge from the problem description and from advice, and argue for the advantages of the explict use of domain knowledge. Then we identify three classes of domain knowledge for which these advantages are most apparent and define a language, DKEL, to represent these classes. DKEL is designed as an extension to PDDL.

  • 16.
    Jonsson, Peter
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, TCSLAB - Theoretical Computer Science Laboratory.
    Haslum, Patrik
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Bäckström, Christer
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, TCSLAB - Theoretical Computer Science Laboratory.
    Towards efficient universal planning: A randomized approach2000In: Artificial Intelligence, ISSN 0004-3702, E-ISSN 1872-7921, Vol. 117, no 1, p. 1-29Article in journal (Refereed)
    Abstract [en]

    One of the most widespread approaches to reactive planning is Schoppers' universal plans. We propose a stricter definition of universal plans which guarantees a weak notion of soundness, not present in the original definition, and isolate three different types of completeness that capture different behaviors exhibited by universal plans. We show that universal plans which run in polynomial time and are of polynomial size cannot satisfy even the weakest type of completeness unless the polynomial hierarchy collapses. By relaxing either the polynomial time or the polynomial space requirement, the construction of universal plans satisfying the strongest type of completeness becomes trivial. As an alternative approach, we study randomized universal planning. By considering a randomized version of completeness and a restricted (but nontrivial) class of problems, we show that there exists randomized universal plans running in polynomial time and using polynomial space which are sound and complete for the restricted class of problems. We also report experimental results on this approach to planning, showing that the performance of a randomized planner is not easily compared to that of a deterministic planner.

  • 17.
    Kvarnström, Jonas
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Doherty, Patrick
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Haslum, Patrik
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Extending TALplanner with concurrency and resources2000In: Proceedings of the 14th European Conference on Artificial Intelligence (ECAI), Amsterdam, The Netherlands: IOS Press , 2000, p. 501-505Conference paper (Refereed)
    Abstract [en]

    We present TALplanner, a forward-chaining planner based on the use of domain-dependent search control knowledge represented as temporal formulas in the Temporal Action Logic (TAL). TAL is a narrative based linear metric time logic used for reasoning about action and change in incompletely specified dynamic environments. TAL is used as the formal semantic basis for TALplanner, where a TAL goal narrative with control formulas is input to TALplanner which then generates a TAL narrative that entails the goal formula. We extend the sequential version of TALplanner, which has previously shown impressive performance on standard benchmarks, in two respects: 1) TALplanner is extended to generate concurrent plans, where operators have varied durations and internal state; and 2) the expressiveness of plan operators is extended for dealing with several different types of resources. The extensions to the planner have been implemented and concurrent planning with resources is demonstrated using an extended logistics benchmark.

  • 18.
    Tjärnström, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Duppils, Mattias
    Linköping University, Department of Electrical Engineering, Electronic Devices. Linköping University, The Institute of Technology.
    Haslum, Patrik
    Linköping University, Department of Computer and Information Science. Linköping University, The Institute of Technology.
    Byers, David
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Kulups, Gundars
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Lawesson, Dan
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    ENSYM-Project Oriented Studies of spring 98 - team 11999Report (Other academic)
    Abstract [en]

    The report is description of the ENSYM Project Oriented Studies(POS) of spring 1998. The project goal was to control a toy cararound a not beforehand given track as fast as possible.

    Download full text (pdf)
    ENSYM-Project Oriented Studies of spring 98 - team 1
    Download full text (ps)
    FULLTEXT01
1 - 18 of 18
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf