D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 50 Citations 10,329 108 World Ranking 2877 National Ranking 113

Research.com Recognitions

Awards & Achievements

2006 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions in knowledge representation, automated planning, utility modeling, and algorithms for SAT and constraint satisfaction.

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Programming language
  • Algorithm

Fahiem Bacchus mainly investigates Artificial intelligence, Theoretical computer science, Machine learning, Algorithm and Representation. His Artificial intelligence research incorporates elements of Domain, State and Temporal logic. His study in the field of Boolean satisfiability problem also crosses realms of DPLL algorithm.

In general Machine learning, his work in Bayesian network and Generalized additive model is often linked to Function linking many areas of study. His Backtracking and Conjunctive normal form study, which is part of a larger body of work in Algorithm, is frequently linked to Small number and Sequence, bridging the gap between disciplines. His work deals with themes such as Interpretation and Complete information, which intersect with Representation.

His most cited work include:

  • LEARNING BAYESIAN BELIEF NETWORKS: AN APPROACH BASED ON THE MDL PRINCIPLE (709 citations)
  • Using temporal logics to express search control knowledge for planning (501 citations)
  • Representing and reasoning with probabilistic knowledge (251 citations)

What are the main themes of his work throughout his whole career to date?

Fahiem Bacchus mostly deals with Artificial intelligence, Theoretical computer science, Algorithm, Solver and Backtracking. His Artificial intelligence research is multidisciplinary, incorporating elements of Domain and Machine learning. Fahiem Bacchus has researched Theoretical computer science in several fields, including Time complexity, Search tree and Markov chain.

Fahiem Bacchus interconnects Constraint learning, Constraint satisfaction, Representation and Mathematical optimization in the investigation of issues within Algorithm. The concepts of his Solver study are interwoven with issues in Maximum satisfiability problem, Parallel computing, True quantified Boolean formula and Boolean satisfiability problem. Constraint logic programming is closely connected to Local consistency in his research, which is encompassed under the umbrella topic of Backtracking.

He most often published in these fields:

  • Artificial intelligence (28.66%)
  • Theoretical computer science (28.66%)
  • Algorithm (28.66%)

What were the highlights of his more recent work (between 2012-2020)?

  • Maximum satisfiability problem (12.74%)
  • Solver (19.11%)
  • Theoretical computer science (28.66%)

In recent papers he was focusing on the following fields of study:

His main research concerns Maximum satisfiability problem, Solver, Theoretical computer science, Mathematical optimization and Satisfiability. His Maximum satisfiability problem research includes themes of Machine learning and Integer programming. His Solver research includes elements of Algorithm, Optimization problem, Boolean satisfiability problem and Parallel computing.

His work carried out in the field of Theoretical computer science brings together such families of science as Probabilistic logic, Representation, State and Expected utility hypothesis. His Satisfiability research integrates issues from Rule of inference, Set, Resolution and Reduced cost. His Backtracking study frequently draws connections to other fields, such as Artificial intelligence.

Between 2012 and 2020, his most popular works were:

  • Maximum satisfiability using core-guided MAXSAT resolution (77 citations)
  • Exploiting the power of MIP solvers in MAXSAT (70 citations)
  • Postponing Optimization to Speed Up MAXSAT Solving (50 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Programming language
  • Algorithm

Fahiem Bacchus focuses on Algorithm, Solver, Satisfiability, Maximum satisfiability problem and Boolean satisfiability problem. His Algorithm research is multidisciplinary, incorporating perspectives in Matching and Property. As part of one scientific family, Fahiem Bacchus deals mainly with the area of Satisfiability, narrowing it down to issues related to the Set, and often Combinatorics, Computation and Duality.

His work in Maximum satisfiability problem addresses subjects such as Mathematical optimization, which are connected to disciplines such as Rule of inference, Resolution and Blocking. His work in Boolean satisfiability problem tackles topics such as Optimization problem which are related to areas like Speedup and Integer programming. His True quantified Boolean formula study necessitates a more in-depth grasp of Theoretical computer science.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

LEARNING BAYESIAN BELIEF NETWORKS: AN APPROACH BASED ON THE MDL PRINCIPLE

Wai Lam;Fahiem Bacchus.
computational intelligence (1994)

1119 Citations

Using temporal logics to express search control knowledge for planning

Fahiem Bacchus;Froduald Kabanza.
Artificial Intelligence (2000)

744 Citations

Representing and reasoning with probabilistic knowledge

Fahiem Ivor Bacchus.
Journal of the Operational Research Society (1988)

655 Citations

Planning for temporally extended goals

Fahiem Bacchus;Froduald Kabanza.
Annals of Mathematics and Artificial Intelligence (1998)

329 Citations

Graphical models for preference and utility

Fahiem Bacchus;Adam Grove.
uncertainty in artificial intelligence (1995)

326 Citations

Representing and reasoning with probabilistic knowledge: a logical approach to probabilities

Fahiem Bacchus.
American Journal of Psychology (1991)

316 Citations

A knowledge-based approach to planning with incomplete information and sensing

Ronald P. A. Petrick;Fahiem Bacchus.
international conference on artificial intelligence planning systems (2002)

293 Citations

From statistical knowledge bases to degrees of belief

Fahiem Bacchus;Adam J. Grove;Joseph Y. Halpern;Daphne Koller.
Artificial Intelligence (1996)

267 Citations

Combining Component Caching and Clause Learning for Effective Model Counting.

Tian Sang;Fahiem Bacchus;Paul Beame;Henry A. Kautz.
theory and applications of satisfiability testing (2004)

255 Citations

UCP-Networks: A Directed Graphical Representation of Conditional Utilities

Craig Boutilier;Fahiem Bacchus;Ronen I. Brafman.
uncertainty in artificial intelligence (2001)

241 Citations

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Best Scientists Citing Fahiem Bacchus

Joao Marques-Silva

Joao Marques-Silva

Centre national de la recherche scientifique, CNRS

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Subbarao Kambhampati

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Arizona State University

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Toby Walsh

Toby Walsh

Commonwealth Scientific and Industrial Research Organisation

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Craig Boutilier

Craig Boutilier

Google (United States)

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Stefan Szeider

Stefan Szeider

TU Wien

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Hector J. Levesque

Hector J. Levesque

University of Toronto

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Moshe Y. Vardi

Moshe Y. Vardi

Rice University

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Giuseppe De Giacomo

Giuseppe De Giacomo

Sapienza University of Rome

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Ronen I. Brafman

Ronen I. Brafman

Ben-Gurion University of the Negev

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Thomas Lukasiewicz

Thomas Lukasiewicz

University of Oxford

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Armin Biere

Armin Biere

Johannes Kepler University of Linz

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Sheila A. McIlraith

Sheila A. McIlraith

University of Toronto

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Derek Long

Derek Long

King's College London

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Dana S. Nau

Dana S. Nau

University of Maryland, College Park

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Joseph Y. Halpern

Joseph Y. Halpern

Cornell University

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Hector Geffner

Hector Geffner

Pompeu Fabra University

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