Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Michael Cashmore"'
Publikováno v:
Proceedings of the International Symposium on Combinatorial Search. 15:305-307
Probabilistic Simple Temporal Networks (PSTN) are used to represent scheduling problems under uncertainty. In a temporal network that is Strongly Controllable (SC) there exists a concrete schedule that is robust to any uncertainty. We solve the probl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::37c0d8f698e772b370dff2a5ce0b2b67
https://doi.org/10.1609/socs.v15i1.21794
https://doi.org/10.1609/socs.v15i1.21794
Autor:
Daniele Magazzeni, Senka Krivic, David E. Smith, Michael Cashmore, Derek Long, Benjamin Krarup
Publikováno v:
Journal of Artificial Intelligence Research. 72:533-612
In automated planning, the need for explanations arises when there is a mismatch between a proposed plan and the user's expectation. We frame Explainable AI Planning in the context of the plan negotiation problem, in which a succession of hypothetica
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::14d0eb28dd13f51db7dee60e666276c3
https://doi.org/10.1613/jair.1.12813
https://doi.org/10.1613/jair.1.12813
Publikováno v:
Proceedings of the International Conference on Automated Planning and Scheduling. 29:665-673
Planning enables intelligent agents, such as robots, to act so as to achieve their long term goals. To make the planning process tractable, a relatively low fidelity model of the world is often used, which sometimes leads to the need to replan. The t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8429172081fbaa26277451fb4a83a35a
https://doi.org/10.1609/icaps.v29i1.3534
https://doi.org/10.1609/icaps.v29i1.3534
Autor:
Stylianos Loukas Vasileiou, William Yeoh, Tran Cao Son, Ashwin Kumar, Michael Cashmore, Dianele Magazzeni
In human-aware planning systems, a planning agent might need to explain its plan to a human user when that plan appears to be non-feasible or sub-optimal. A popular approach, called model reconciliation, has been proposed as a way to bring the model
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::247ad85852e6c2b679217ca5c30cf858
https://strathprints.strath.ac.uk/81642/1/Vasileiou_etal_JAIR_2022_A_logic_based_explanation_generation_framework_for_classical.pdf
https://strathprints.strath.ac.uk/81642/1/Vasileiou_etal_JAIR_2022_A_logic_based_explanation_generation_framework_for_classical.pdf
Publikováno v:
AAAI
In many usage scenarios of AI Planning technology, users will want not just a plan π but an explanation of the space of possible plans, justifying π. In particular, in oversubscription planning where not all goals can be achieved, users may ask why
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a6e92e3ac93d4fd6eaf5a4f3bdff8f9b
https://doi.org/10.1609/aaai.v34i06.6534
https://doi.org/10.1609/aaai.v34i06.6534
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030948757
Automated planning is able to handle increasingly complex applications, but can produce unsatisfactory results when the goal and metric provided in its model does not match the actual expectation and preference of those using the tool. This can be am
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::47817d343a45d69641b62106d7717d0c
https://strathprints.strath.ac.uk/80038/1/Murray_etal_LNCS_2022_Towards_temporally_uncertain_explainable_AI_planning.pdf
https://strathprints.strath.ac.uk/80038/1/Murray_etal_LNCS_2022_Towards_temporally_uncertain_explainable_AI_planning.pdf
Publikováno v:
Proceedings of the International Conference on Automated Planning and Scheduling
King's College London
Association for the Advancement of Artificial Intelligence Spring Conference Series
King's College London
Association for the Advancement of Artificial Intelligence Spring Conference Series
To achieve a complex task, a robot often needs to navigate in a physical space in order to complete activities in different locations. For example, it may need to inspect several structures, making multiple observations of each structure from differe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7b1b7b0e99d9422cdbdc7d73e191eb02
https://strathprints.strath.ac.uk/77086/1/Keren_etal_ICAPS_2021_Task_aware_waypoint_sampling_for_robotic_planning.pdf
https://strathprints.strath.ac.uk/77086/1/Keren_etal_ICAPS_2021_Task_aware_waypoint_sampling_for_robotic_planning.pdf
Publikováno v:
AAAI
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence
Scopus-Elsevier
Magazzeni, D, Cashmore, M, Cimatti, A, Micheli, A & Zehtabi, P 2019, Robustness Envelopes for Temporal Plans . in Proceedings of AAAI Conference on Artificial Intelligence (AAAI 2019) . https://doi.org/10.1609/aaai.v33i01.33017538
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence
Scopus-Elsevier
Magazzeni, D, Cashmore, M, Cimatti, A, Micheli, A & Zehtabi, P 2019, Robustness Envelopes for Temporal Plans . in Proceedings of AAAI Conference on Artificial Intelligence (AAAI 2019) . https://doi.org/10.1609/aaai.v33i01.33017538
To achieve practical execution, planners must produce temporal plans with some degree of run-time adaptability. Such plans can be expressed as Simple Temporal Networks (STN), that constrain the timing of action activations, and implicitly represent t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1604e1fd598ec7ca8da12e70333e080b
https://doi.org/10.1609/aaai.v33i01.33017538
https://doi.org/10.1609/aaai.v33i01.33017538
Autor:
Marios Daoutis, Aneta Vulgarakis Feljan, Marin Orlic, Michael Cashmore, Daniele Magazzeni, Dorian Buksz, Swarup Kumar Mohalik, Ramamurthy Badrinath, Anusha Mujumdar
Publikováno v:
IROS
Realization of industry-scale, goal-driven, autonomous systems with AI planning technology faces several challenges: flexibly specifying planning goal states in varying situations, synthesizing plans in large state spaces, re-planning in dynamic situ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::55a474c966f55afb7f90e6e8a4674949
https://strathprints.strath.ac.uk/73423/1/Buksz_etal_IROS_2020_Intent_driven_strategic_tactical_planning_for_autonomous_site_inspection.pdf
https://strathprints.strath.ac.uk/73423/1/Buksz_etal_IROS_2020_Intent_driven_strategic_tactical_planning_for_autonomous_site_inspection.pdf
Publikováno v:
Cashmore, M, Magazzeni, D & Zehtabi, P 2020, ' Planning for Hybrid Systems via Satisfiability Modulo Theories ', Journal Artificial Intelligence Research, vol. 67, pp. 253-283 . https://doi.org/10.1613/jair.1.11751
Planning for hybrid systems is important for dealing with real-world applications, and PDDL+ supports this representation of domains with mixed discrete and continuous dynamics. In this paper we present a new approach for planning for hybrid systems,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a9e3e3e19823d7490f01fe0be6bbd695
https://kclpure.kcl.ac.uk/ws/files/127653214/Planning_for_Hybrid_Systems_CASHMORE_Acc16NovEpub19Feb2020_GREEN_VoR.pdf
https://kclpure.kcl.ac.uk/ws/files/127653214/Planning_for_Hybrid_Systems_CASHMORE_Acc16NovEpub19Feb2020_GREEN_VoR.pdf