Artificial intelligence, Probabilistic logic, Machine learning, Process and Plan recognition are his primary areas of study. His Artificial intelligence study incorporates themes from Imputation and Natural language processing. The study of Machine learning is intertwined with the study of Classifier in a number of ways.
In his work, Planner, Theoretical computer science, Action, Explicit knowledge and Nondeterministic algorithm is strongly intertwined with Context, which is a subfield of Software engineering. His Planner study combines topics in areas such as Algorithm and Semantics. His Bayesian network research is multidisciplinary, incorporating elements of Natural language, Bayesian statistics, Bayesian inference and Bayes' theorem.
Robert P. Goldman mainly investigates Artificial intelligence, Probabilistic logic, Planner, Control and Software engineering. His Artificial intelligence study frequently draws parallels with other fields, such as Machine learning. The study incorporates disciplines such as Theoretical computer science, Plan recognition, Parameterized complexity and Extension in addition to Probabilistic logic.
His biological study spans a wide range of topics, including Hierarchical task network, Planning algorithms, Action and Image processing. His Control research is multidisciplinary, incorporating perspectives in Abstraction and Task. Robert P. Goldman interconnects Selection, Bayesian statistics, Knowledge base and Knowledge representation and reasoning in the investigation of issues within Bayesian network.
Artificial intelligence, Probabilistic logic, Theoretical computer science, Intrusion detection system and Plan recognition are his primary areas of study. His research integrates issues of Machine learning and Management science in his study of Artificial intelligence. When carried out as part of a general Probabilistic logic research project, his work on Probabilistic model checking is frequently linked to work in Simple, therefore connecting diverse disciplines of study.
Many of his research projects under Theoretical computer science are closely connected to Bounded function with Bounded function, tying the diverse disciplines of science together. His Intrusion detection system study which covers Active perception that intersects with Software deployment, Value of information and Server. His biological study deals with issues like Software engineering, which deal with fields such as Context.
His main research concerns Artificial intelligence, Satisfiability modulo theories, Hybrid system, Nonlinear system and Planner. Robert P. Goldman has researched Artificial intelligence in several fields, including Technical report, Management science and Knowledge management. His Satisfiability modulo theories research integrates issues from Planning Domain Definition Language, Control theory and Solver, Mathematical optimization, Heuristics.
The concepts of his Hybrid system study are interwoven with issues in Variable, Computational complexity theory and Control engineering. His Planner research is multidisciplinary, relying on both Domain, Preprocessor and Theoretical computer science, Nondeterministic algorithm.
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A Bayesian model of plan recognition
Eugene Charniak;Robert P. Goldman.
Artificial Intelligence (1993)
From knowledge bases to decision models
Michael P. Wellman;John S. Breese;Robert P. Goldman.
Plan recognition in intrusion detection systems
C.W. Geib;R.P. Goldman.
darpa information survivability conference and exposition (2001)
A probabilistic plan recognition algorithm based on plan tree grammars
Christopher W. Geib;Robert P. Goldman.
Artificial Intelligence (2009)
Plan, Activity, and Intent Recognition: Theory and Practice
Gita Sukthankar;Christopher Geib;Hung Hai Bui;David Pynadath.
Plan, Activity, and Intent Recognition: Theory and Practice 1st (2014)
Probabilistic Abduction for Plan Recognition
Eugene Charniak;Robert Goldman.
A new model of plan recognition
Robert P. Goldman;Christopher W. Geib;Christopher A. Miller.
uncertainty in artificial intelligence (1999)
Imputation of missing data using machine learning techniques
Kamakshi Lakshminarayan;Steven A. Harp;Robert Goldman;Tariq Samad.
knowledge discovery and data mining (1996)
Expressive planning and explicit knowledge
Robert P. Goldman;Mark S. Boddy.
international conference on artificial intelligence planning systems (1996)
A semantics for probabilistic quantifier-free first-order languages, with particular application to story understanding
Eugene Charniak;Robert Goldman.
international joint conference on artificial intelligence (1989)
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