The Experts below are selected from a list of 7746 Experts worldwide ranked by ideXlab platform

Jonas Kvarnström - One of the best experts on this subject based on the ideXlab platform.

  • ICAPS - Planning for loosely coupled agents using partial order Forward-Chaining
    2011
    Co-Authors: Jonas Kvarnström
    Abstract:

    We investigate a hybrid between temporal partial-order and Forward-Chaining planning where each action in a partially ordered plan is associated with a partially defined state. The focus is on centralized planning for multi-agent domains and on loose commitment to the precedence between actions belonging to distinct agents, leading to execution schedules that are flexible where it matters the most. Each agent, on the other hand, has a sequential thread of execution reminiscent of Forward-Chaining. This results in strong and informative agent-specific partial states that can be used for partial evaluation of preconditions as well as precondition control formulas used as guidance. Empirical evaluation shows the resulting planner to be competitive with TLPLAN and TALplanner, two other planners based on control formulas, while using a considerably more expressive and flexible plan structure.

  • planning for loosely coupled agents using partial order Forward Chaining
    International Conference on Automated Planning and Scheduling, 2011
    Co-Authors: Jonas Kvarnström
    Abstract:

    We investigate a hybrid between temporal partial-order and Forward-Chaining planning where each action in a partially ordered plan is associated with a partially defined state. The focus is on centralized planning for multi-agent domains and on loose commitment to the precedence between actions belonging to distinct agents, leading to execution schedules that are flexible where it matters the most. Each agent, on the other hand, has a sequential thread of execution reminiscent of Forward-Chaining. This results in strong and informative agent-specific partial states that can be used for partial evaluation of preconditions as well as precondition control formulas used as guidance. Empirical evaluation shows the resulting planner to be competitive with TLPLAN and TALplanner, two other planners based on control formulas, while using a considerably more expressive and flexible plan structure.

  • TALplanner: an empirical investigation of a temporal logic-based Forward Chaining planner
    Proceedings. Sixth International Workshop on Temporal Representation and Reasoning. TIME-99, 1999
    Co-Authors: P Doherty, Jonas Kvarnström
    Abstract:

    We present a new Forward Chaining planner, TALplanner, based on ideas developed by Bacchus (1998) and Kabanza (1997), where domain-dependent search control knowledge represented as temporal formulas is used to effectively control Forward Chaining. Instead of using a linear modal tense logic as with Bacchus and Kabanza, we use TAL, a narrative-based linear temporal logic used for reasoning about action and change in incompletely specified dynamic environments. Two versions of TALplanner are considered, TALplan/modal which is based on the use of emulated modal formulas and a progression algorithm, and TALplan/non-modal which uses neither modal formulas nor a progression algorithm. For both versions of TALplanner and for all tested domains, TALplanner is shown to be considerably faster and requires less memory. The TAL versions also permit the representation of durative actions with internal state.

  • TIME - TALplanner: an empirical investigation of a temporal logic-based Forward Chaining planner
    Proceedings. Sixth International Workshop on Temporal Representation and Reasoning. TIME-99, 1
    Co-Authors: P Doherty, Jonas Kvarnström
    Abstract:

    We present a new Forward Chaining planner, TALplanner, based on ideas developed by Bacchus (1998) and Kabanza (1997), where domain-dependent search control knowledge represented as temporal formulas is used to effectively control Forward Chaining. Instead of using a linear modal tense logic as with Bacchus and Kabanza, we use TAL, a narrative-based linear temporal logic used for reasoning about action and change in incompletely specified dynamic environments. Two versions of TALplanner are considered, TALplan/modal which is based on the use of emulated modal formulas and a progression algorithm, and TALplan/non-modal which uses neither modal formulas nor a progression algorithm. For both versions of TALplanner and for all tested domains, TALplanner is shown to be considerably faster and requires less memory. The TAL versions also permit the representation of durative actions with internal state.

Amanda Smith - One of the best experts on this subject based on the ideXlab platform.

  • a new local search algorithm for Forward Chaining planning
    International Conference on Automated Planning and Scheduling, 2007
    Co-Authors: Andrew Coles, Maria Fox, Amanda Smith
    Abstract:

    Forward-Chaining heuristic search is a well-established and popular paradigm for domain-independent planning. Its effectiveness relies on the heuristic information provided by a state evaluator, and the search algorithm used with this in order to solve the problem. This paper presents a new stochastic local-search algorithm for Forward-Chaining planning. The algorithm is used as the basis of a planner in conjunction with FF's Relaxed Planning Graph heuristic. Our approach is unique in that localised restarts are used, returning to the start of plateaux and saddle points, as well as global restarts to the initial state. The majority of the search time when using FF's 'Enforced Hill Climbing' is spent using breadth-first search to escape local minima. Our localised restarts, in conjunction with stochastic search, serve to replace this expensive breadth-first search step. We also describe an extended search neighbourhood incorporating non-helpful actions and the 'lookahead' states used in YAHSP. Making use of non-helpful actions and stochastic search allows us to restart the local-search from the initial state when dead-ends are encountered; rather than resorting to best-first search. We present analyses to demonstrate the effectiveness of our restart strategies, along with results that show the new planning algorithm is effective across a range of domains.

  • ICAPS - A new local-search algorithm for Forward-Chaining planning
    2007
    Co-Authors: Andrew Coles, Maria Fox, Amanda Smith
    Abstract:

    Forward-Chaining heuristic search is a well-established and popular paradigm for domain-independent planning. Its effectiveness relies on the heuristic information provided by a state evaluator, and the search algorithm used with this in order to solve the problem. This paper presents a new stochastic local-search algorithm for Forward-Chaining planning. The algorithm is used as the basis of a planner in conjunction with FF's Relaxed Planning Graph heuristic. Our approach is unique in that localised restarts are used, returning to the start of plateaux and saddle points, as well as global restarts to the initial state. The majority of the search time when using FF's 'Enforced Hill Climbing' is spent using breadth-first search to escape local minima. Our localised restarts, in conjunction with stochastic search, serve to replace this expensive breadth-first search step. We also describe an extended search neighbourhood incorporating non-helpful actions and the 'lookahead' states used in YAHSP. Making use of non-helpful actions and stochastic search allows us to restart the local-search from the initial state when dead-ends are encountered; rather than resorting to best-first search. We present analyses to demonstrate the effectiveness of our restart strategies, along with results that show the new planning algorithm is effective across a range of domains.

Dwi Kurniawan - One of the best experts on this subject based on the ideXlab platform.

  • SISTEM PAKAR UNTUK DIAGNOSA PENYAKIT TANAMAN MENTIMUNDENGAN METODE Forward Chaining
    2016
    Co-Authors: Dwi Kurniawan
    Abstract:

    ABSTRAK Sistem pakar adalah sistem yang berusaha mengadopsi pengetahuan manusia ke komputer, agar komputer dapat menyelesaikan masalah seperti yang biasa dilakukan oleh para ahli. Dengan sistem pakar ini, orang awam pun dapat menyelesaikan masalah yang cukup rumit yang sebenarnya hanya bisa diselesaikan dengan bantuan para ahli. Bagi para ahli, sistem pakar juga akan membantu aktivitasnya sebagai asisten yang sangat berpengalaman. sistem yang didesain dan diimplementasikan dengan bantuan bahasa pemrograman tertentu untuk dapat menyelesaikan masalah seperti yang dilakukan oleh para ahli/pakar. Metode inferensi Forward Chaining adalah metode penalaran yang terdapat pada sistem pakar, cara kerja Forward Chaining adalah dengan melakukan penalaran secara maju sehingga sistem pakar akan melakukan diagnosa penyakit dari hasil input gejala yang dimasukkan oleh user. memudahkan user dalam melakukan proses konsultasi, karena pertanyaan gejala yang diajukan hanya terkait penyakit yang dialami. Selain itu sistem pakar ini juga memudahkan bagi admin untuk melakukan update basis aturan, karena adanya fitur halaman edit basis aturan yang dapat digunakan untuk menambah, mengupdate dan menghapus penyakit, gejala dan pengobatannya. Editor yang di gunakan dalam membangun aplikasi untuk admin menggunakan Macromedia Dreamweaver, bahasa pemrograman PHP (Hypertext Preprocessor), dan MySQL sebagai database. Aplikasi sistem pakar yang dibuat pada tugas akhir ini berbasis website. Untuk user dan website untuk admin. Dengan alasan memudahkan para petani khususnya petani mentimun. Kata kunci : aplikasi, sistem pakar, Forward Chaining, diagnosa.

Andrew Coles - One of the best experts on this subject based on the ideXlab platform.

  • a new local search algorithm for Forward Chaining planning
    International Conference on Automated Planning and Scheduling, 2007
    Co-Authors: Andrew Coles, Maria Fox, Amanda Smith
    Abstract:

    Forward-Chaining heuristic search is a well-established and popular paradigm for domain-independent planning. Its effectiveness relies on the heuristic information provided by a state evaluator, and the search algorithm used with this in order to solve the problem. This paper presents a new stochastic local-search algorithm for Forward-Chaining planning. The algorithm is used as the basis of a planner in conjunction with FF's Relaxed Planning Graph heuristic. Our approach is unique in that localised restarts are used, returning to the start of plateaux and saddle points, as well as global restarts to the initial state. The majority of the search time when using FF's 'Enforced Hill Climbing' is spent using breadth-first search to escape local minima. Our localised restarts, in conjunction with stochastic search, serve to replace this expensive breadth-first search step. We also describe an extended search neighbourhood incorporating non-helpful actions and the 'lookahead' states used in YAHSP. Making use of non-helpful actions and stochastic search allows us to restart the local-search from the initial state when dead-ends are encountered; rather than resorting to best-first search. We present analyses to demonstrate the effectiveness of our restart strategies, along with results that show the new planning algorithm is effective across a range of domains.

  • ICAPS - A new local-search algorithm for Forward-Chaining planning
    2007
    Co-Authors: Andrew Coles, Maria Fox, Amanda Smith
    Abstract:

    Forward-Chaining heuristic search is a well-established and popular paradigm for domain-independent planning. Its effectiveness relies on the heuristic information provided by a state evaluator, and the search algorithm used with this in order to solve the problem. This paper presents a new stochastic local-search algorithm for Forward-Chaining planning. The algorithm is used as the basis of a planner in conjunction with FF's Relaxed Planning Graph heuristic. Our approach is unique in that localised restarts are used, returning to the start of plateaux and saddle points, as well as global restarts to the initial state. The majority of the search time when using FF's 'Enforced Hill Climbing' is spent using breadth-first search to escape local minima. Our localised restarts, in conjunction with stochastic search, serve to replace this expensive breadth-first search step. We also describe an extended search neighbourhood incorporating non-helpful actions and the 'lookahead' states used in YAHSP. Making use of non-helpful actions and stochastic search allows us to restart the local-search from the initial state when dead-ends are encountered; rather than resorting to best-first search. We present analyses to demonstrate the effectiveness of our restart strategies, along with results that show the new planning algorithm is effective across a range of domains.

Ahmad Fauzi - One of the best experts on this subject based on the ideXlab platform.

  • penerapan Forward Chaining dalam sistem pakar untuk mendiagnosis penyakit pada anak
    Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi, 2016
    Co-Authors: Ahmad Fauzi
    Abstract:

    Abstrak Anak merupakan harapan bagi orang tua. Kesehatan anak menjadi perhatian penting agar anak dapat tumbuh dan berkembang sesuai harapan. Keterbatasan pengetahuan orang tua dan biaya konsultasi kesehatan yang relatif mahal menimbulkan kebutuhan akses akan pengetahuan kepakaran tersebut. Sistem pakar untuk mendiagnosis penyakit pada anak dirancang menggunakan metode pengembangan sistem Expert System Development Life Cycle (ESDLC) dengan penerapan teknik penalaran inferensi Forward Chaining. Penalaran untuk menentukan kesimpulan dimulai dari gejala penyakit sebagai fakta. Gambaran inferensi dapat terlihat pada decission tree dari sistem yang dirancang. Sistem pakar telah menggunakan basis pengetahuan untuk menentukan penyakit. Kata Kunci : Expert System, Forward Chaining, decission tree.