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Intention Recognition for Multiple Agents. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.01.066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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Weerawardhana S, Whitley D, Roberts M. Models of Intervention: Helping Agents and Human Users Avoid Undesirable Outcomes. Front Artif Intell 2022; 4:723936. [PMID: 35187470 PMCID: PMC8851243 DOI: 10.3389/frai.2021.723936] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 12/23/2021] [Indexed: 11/13/2022] Open
Abstract
When working in an unfamiliar online environment, it can be helpful to have an observer that can intervene and guide a user toward a desirable outcome while avoiding undesirable outcomes or frustration. The Intervention Problem is deciding when to intervene in order to help a user. The Intervention Problem is similar to, but distinct from, Plan Recognition because the observer must not only recognize the intended goals of a user but also when to intervene to help the user when necessary. We formalize a family of Intervention Problems and show that how these problems can be solved using a combination of Plan Recognition methods and classification algorithms to decide whether to intervene. For our benchmarks, the classification algorithms dominate three recent Plan Recognition approaches. We then generalize these results to Human-Aware Intervention, where the observer must decide in real time whether to intervene human users solving a cognitively engaging puzzle. Using a revised feature set more appropriate to human behavior, we produce a learned model to recognize when a human user is about to trigger an undesirable outcome. We perform a human-subject study to evaluate the Human-Aware Intervention. We find that the revised model also dominates existing Plan Recognition algorithms in predicting Human-Aware Intervention.
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Affiliation(s)
- Sachini Weerawardhana
- Department of Computer Science, Colorado State University, Fort Collins, CO, United States
- *Correspondence: Sachini Weerawardhana
| | - Darrell Whitley
- Department of Computer Science, Colorado State University, Fort Collins, CO, United States
| | - Mark Roberts
- The U.S. Naval Research Laboratory, Washington, DC, United States
- Mark Roberts
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Bäckström C, Jonsson P. A framework for analysing state-abstraction methods. ARTIF INTELL 2022. [DOI: 10.1016/j.artint.2021.103608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Urbanovská M, Komenda A. Neural networks for model-free and scale-free automated planning. Knowl Inf Syst 2021. [DOI: 10.1007/s10115-021-01619-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Chrpa L, Pilát M, Gemrot J. Planning and acting in dynamic environments: identifying and avoiding dangerous situations. J EXP THEOR ARTIF IN 2021. [DOI: 10.1080/0952813x.2021.1938697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Lukáš Chrpa
- Faculty of Electrical Engineering, Czech Technical University in Prague, Prague 6, Czech Republic
| | - Martin Pilát
- Faculty of Mathematics and Physics, Charles University, Prague 2, Czech Republic
| | - Jakub Gemrot
- Faculty of Mathematics and Physics, Charles University, Prague 2, Czech Republic
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Abstract
Abstract
In the reinforcement learning context, a landmark is a compact information which uniquely couples a state, for problems with hidden states. Landmarks are shown to support finding good memoryless policies for Partially Observable Markov Decision Processes (POMDP) which contain at least one landmark. SarsaLandmark, as an adaptation of Sarsa(λ), is known to promise a better learning performance with the assumption that all landmarks of the problem are known in advance.
In this paper, we propose a framework built upon SarsaLandmark, which is able to automatically identify landmarks within the problem during learning without sacrificing quality, and requiring no prior information about the problem structure. For this purpose, the framework fuses SarsaLandmark with a well-known multiple-instance learning algorithm, namely Diverse Density (DD). By further experimentation, we also provide a deeper insight into our concept filtering heuristic to accelerate DD, abbreviated as DDCF (Diverse Density with Concept Filtering), which proves itself to be suitable for POMDPs with landmarks. DDCF outperforms its antecedent in terms of computation speed and solution quality without loss of generality.
The methods are empirically shown to be effective via extensive experimentation on a number of known and newly introduced problems with hidden state, and the results are discussed.
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Bechon P, Lesire C, Barbier M. Hybrid planning and distributed iterative repair for multi-robot missions with communication losses. Auton Robots 2019. [DOI: 10.1007/s10514-019-09869-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Chrpa L, Vallati M, McCluskey TL. Inner entanglements: Narrowing the search in classical planning by problem reformulation. Comput Intell 2019. [DOI: 10.1111/coin.12203] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Lukáš Chrpa
- Faculty of Electrical EngineeringCzech Technical University in Prague Prague Czech Republic
- Faculty of Mathematics and PhysicsCharles University Prague Czech Republic
| | - Mauro Vallati
- School of Computing and EngineeringUniversity of Huddersfield Huddersfield UK
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Abstract
AbstractGeneralized planningstudies the representation, computation and evaluation of solutions that are valid for multiple planning instances. These are topics studied since the early days of AI. However, in recent years, we are experiencing the appearance of novel formalisms to compactly represent generalized planning tasks, the solutions to these tasks (calledgeneralized plans) and efficient algorithms to compute generalized plans. The paper reviews recent advances in generalized planning and relates them to existing planning formalisms, such asplanning with domain control knowledgeand approaches forplanning under uncertainty, that also aim at generality.
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Chrpa L, Vallati M, McCluskey TL. Outer entanglements: a general heuristic technique for improving the efficiency of planning algorithms. J EXP THEOR ARTIF IN 2018. [DOI: 10.1080/0952813x.2018.1509377] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- Lukáš Chrpa
- Department of Computer Science, Czech Technical University in Prague, Prague, Czech Republic
- Department of Theoretical Computer Science and Mathematical Logic, Charles University in Prague, Prague, Czech Republic
| | - Mauro Vallati
- Department of Informatics, University of Huddersfield, Queensgate, UK
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Ponzoni Carvalho Chanel C, Albore A, T’Hooft J, Lesire C, Teichteil-Königsbuch F. AMPLE: an anytime planning and execution framework for dynamic and uncertain problems in robotics. Auton Robots 2018. [DOI: 10.1007/s10514-018-9703-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Affiliation(s)
- D. Pellier
- LIG, Université Grenoble Alpes, Grenoble, France
| | - H. Fiorino
- LIG, Université Grenoble Alpes, Grenoble, France
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Abstract
Many Artificial Intelligence techniques have been developed for intelligent and autonomous systems to act and make rational decisions based on perceptions of the world state. Among these techniques, HTN (Hierarchical Task Network) planning is one of the most used in practice. HTN planning is based on expressive languages allowing to specify complex expert knowledge for real world domains. At the same time, many preprocessing techniques for classical planning were proposed to speed up the search. One of these technique, named grounding, consists in enumerating and instantiating all the possible actions from the planning problem descriptions. This technique has proven its effectiveness. Therefore, combining the expressiveness of HTN planning with the efficiency of the grounding preprocessing techniques used in classical planning is a very challenging issue. In this paper, we propose a generic algorithm to ground the domain representation for HTN planning. We show experimentally that grounding process improves the performances of state of the art HTN planners on a range of planning problems from the International Planning Competition (IPC).
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Affiliation(s)
- Abdeldjalil Ramoul
- University Grenoble Alpes, LIG, F-38000 Grenoble, France
- Cloud Temple, 215 Avenue Georges Clemenceau, 92024, Nanterre Cedex, France
| | - Damien Pellier
- University Grenoble Alpes, LIG, F-38000 Grenoble, France
| | | | - Sylvie Pesty
- University Grenoble Alpes, LIG, F-38000 Grenoble, France
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LinGraph: a graph-based automated planner for concurrent task planning based on linear logic. APPL INTELL 2017. [DOI: 10.1007/s10489-017-0936-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Abstract
AbstractMost of the current top-performing planners are sequential planners that only handle total-order plans. Although this is a computationally efficient approach, the management of total-order plans restrict the choices of reasoning and thus the generation of flexible plans. In this paper, we present FLAP2, a forward-chaining planner that follows the principles of the classical POCL (Partial-Order Causal-Link Planning) paradigm. Working with partial-order plans allows FLAP2 to easily manage the parallelism of the plans, which brings several advantages: more flexible executions, shorter plan durations (makespan) and an easy adaptation to support new features like temporal or multi-agent planning. However, one of the limitations of POCL planners is that they require far more computational effort to deal with the interactions that arise among actions. FLAP2 minimizes this overhead by applying several techniques that improve its performance: the combination of different state-based heuristics and the use of parallel processes to diversify the search in different directions when a plateau is found. To evaluate the performance of FLAP2, we have made a comparison with four state-of-the-art planners: SGPlan, YAHSP2, Temporal Fast Downward and OPTIC. Experimental results show that FLAP2 presents a very acceptable trade-off between time and quality and a high coverage on the current planning benchmarks.
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Marzal E, Sebastia L, Onaindia E. Temporal landmark graphs for solving overconstrained planning problems. Knowl Based Syst 2016. [DOI: 10.1016/j.knosys.2016.05.029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Alcázar V, Fernández S, Borrajo D, Veloso M. Using random sampling trees for automated planning. AI COMMUN 2015. [DOI: 10.3233/aic-150658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Vidal Alcázar
- Computer Science Department, Universidad Carlos III de Madrid, Madrid, Spain. E-mails: , ,
| | - Susana Fernández
- Computer Science Department, Universidad Carlos III de Madrid, Madrid, Spain. E-mails: , ,
| | - Daniel Borrajo
- Computer Science Department, Universidad Carlos III de Madrid, Madrid, Spain. E-mails: , ,
| | - Manuela Veloso
- Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA. E-mail:
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Zhang L, Wang CJ, Xie JY. Cost optimal planning with multi-valued landmarks. AI COMMUN 2015. [DOI: 10.3233/aic-140622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Lei Zhang
- State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China. E-mails: , ,
| | - Chong-Jun Wang
- State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China. E-mails: , ,
| | - Jun-Yuan Xie
- State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China. E-mails: , ,
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Transfer of Domain Knowledge in Plan Generation: Learning Goal-dependent Annulling Conditions for Actions. KUNSTLICHE INTELLIGENZ 2014. [DOI: 10.1007/s13218-013-0282-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Luo J, Zhu C, Zhang W, Liu Z. Planning with Multistep Forward Search with Forced Goal-Ordering Constraints. Comput Intell 2013. [DOI: 10.1111/coin.12019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Jiangfeng Luo
- Science and Technology on Information Systems Engineering Laboratory; National University of Defense Technology; Changsha China
| | - Cheng Zhu
- Science and Technology on Information Systems Engineering Laboratory; National University of Defense Technology; Changsha China
| | - Weiming Zhang
- Science and Technology on Information Systems Engineering Laboratory; National University of Defense Technology; Changsha China
| | - Zhong Liu
- Science and Technology on Information Systems Engineering Laboratory; National University of Defense Technology; Changsha China
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Geffner H. Computational models of planning. WILEY INTERDISCIPLINARY REVIEWS. COGNITIVE SCIENCE 2013; 4:341-356. [DOI: 10.1002/wcs.1233] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Heuristic Search for Planning with Different Forced Goal-Ordering Constraints. ScientificWorldJournal 2013; 2013:963874. [PMID: 23935443 PMCID: PMC3725708 DOI: 10.1155/2013/963874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2013] [Accepted: 06/11/2013] [Indexed: 11/18/2022] Open
Abstract
Planning with forced goal-ordering (FGO) constraints has been proposed many times over the years, but there are still major difficulties in realizing these FGOs in plan generation. In certain planning domains, all the FGOs exist in the initial state. No matter which approach is adopted to achieve a subgoal, all the subgoals should be achieved in a given sequence from the initial state. Otherwise, the planning may arrive at a deadlock.
For some other planning domains, there is no FGO in the initial state. However, FGO may occur during the planning process if certain subgoal is achieved by an inappropriate approach. This paper contributes to illustrate that it is the excludable constraints among the goal achievement operations (GAO) of different subgoals that introduce the FGOs into the planning problem, and planning with FGO is still a challenge for the heuristic search based planners. Then, a novel multistep forward search algorithm is proposed which can solve the planning problem with different FGOs efficiently.
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Abstract
AbstractRecent discoveries in automated planning are broadening the scope of planners, from toy problems to real applications. However, applying automated planners to real-world problems is far from simple. On the one hand, the definition of accurate action models for planning is still a bottleneck. On the other hand, off-the-shelf planners fail to scale-up and to provide good solutions in many domains. In these problematic domains, planners can exploit domain-specific control knowledge to improve their performance in terms of both speed and quality of the solutions. However, manual definition of control knowledge is quite difficult. This paper reviews recent techniques in machine learning for the automatic definition of planning knowledge. It has been organized according to the target of the learning process: automatic definition of planning action models and automatic definition of planning control knowledge. In addition, the paper reviews the advances in the related field of reinforcement learning.
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Abstract
AbstractThere are many approaches for solving planning problems. Many of these approaches are based on ‘brute force’ search methods and they usually do not care about structures of plans previously computed in particular planning domains. By analyzing these structures, we can obtain useful knowledge that can help us find solutions to more complex planning problems. The method described in this paper is designed for gathering macro-operators by analyzing training plans. This sort of analysis is based on the investigation of action dependencies in training plans. Knowledge gained by our method can be passed directly to planning algorithms to improve their efficiency.
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Pizzi D, Lugrin J, Whittaker A, Cavazza M. Automatic Generation of Game Level Solutions as Storyboards. IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES 2010. [DOI: 10.1109/tciaig.2010.2070066] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Controlling Narrative Generation with Planning Trajectories: The Role of Constraints. INTERACTIVE STORYTELLING 2009. [DOI: 10.1007/978-3-642-10643-9_28] [Citation(s) in RCA: 29] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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