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Richard Sherl

Richard Scherl, Ph.D.

  • Associate Professor

Department: Computer Science and Software Engineering

Office: James and Marlene Howard Hall 222

Phone: 732-571-4457

Email: rscherl@monmouth.edu


The focus of Professor Scherl’s research is on logic-based methods of representing common sense knowledge about the world and the development of automated methods for reasoning with these representations. He pursues connections between this topic and work in other areas of computing such as databases and natural language processing, as well as other disciplines such as linguistics, philosophy, and the social sciences.

Professor Scherl has been teaching at Monmouth University since 2002. He teaches courses in the areas of artificial intelligence, databases, discrete mathematics, and programming.

Education

Ph.D., University of Illinois, Computer Science

MA & Ph.D., University of Chicago, Anthropology

Diploma, Madurai University (India), Tamil

Research Interests

Artificial Intelligence (Knowledge Representation, Automated Reasoning, Natural Language Processing), Cognitive Science, Information Integration and Data Management, Computational Social Sciences

Books

R.B. Scherl, Sections on “ Data Mining Fundamentals,” “Classification Methods,” and “Outlier Detection” in   “Data Mining,” Chapter 19  of the   Handbook of Discrete and Combinatorial Mathematics (2nd Edition), edited by Kenneth Rosen and Douglas Shier. CRC Press 2018

Scholarly Articles

R.B. Scherl, “A situation calculus model of knowledge and belief based on thinking about justifications,” Proceedings of NMR 2022, 20th International Workshop on Non-Monotonic Reasoning, August 2022, Haifa, Israel.

R.B. Scherl, “A situation calculus model of linguistic context,” Proceedings of AIC 2022, 8th International Workshop on Artificial Intelligence and Cognition, June 2022, Örebro, Sweden.

R.B. Scherl, “A situation calculus model of conversational actions,” Extended Abstract, Proceedings of KR4HI , 1st International Workshop on Knowledge Representation for Hybrid Intelligence, June 14, 2022, Amsterdam, Netherlands.

Chitta Baral, Hans van Ditmarsch, Jan van Eijck, Esra Erdem, Andreas Herzig, Mathias Justesen, Yongmei Liu, Richard Scherl and Tran Cao Son. “Exploring the Relations Between Knowledge and Belief in Multi-Agent Epistemic Planning, pages 23-27 in Epsitemic Planning, Report from Dagstuhl Seminar 17231, edited by Chitta Baral, Thomas Bolander, Hans van Ditmarsch, and Sheila McIlraith. July 2017, Report from Seminar June 5-9 2017

Scherl, Richard. “Towards a Formal Model of the Decitically Constructed Context of Narratives.” Pages 520-525 in the proceedings of CONTEXT 2015. November 2-6. Larnaca, Cyprus.

Somak Aditya, Chitta Baral, Nguyen Ha Vo, Joohyung Lee, Jieping Ye, Zaw Naung, Barry Lumpkin, Jenny Hastings, Richard Scherl, Dawn M. Sweet and Daniela Inclezan. “Recognizing Social Constructs from Textual Conversation.” Pages 1293-1298 in Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. May 31-June 5, 2015. Denver, Colorado.

Scherl, Richard. “A Situation-Calculus Based Theory of Justified Knowledge and Action.” Pages 134-140 in Logical Formalizations of Commonsense Reasoning: Papers from the 2015 AAAI Spring Symposium. Technical Report SS-15-04. AAAI Press. Palo Alto, California. 2015.

Scherl, R. “Developing Semantic Classifiers for Big Data. In proceedings of AAAI Fall Symposium on Semantics for Big Data.” Arlington, Virgina. November 15-17, 2013.

Scherl, R., Inclezan, D. and Gelfond, M. “Automated Inference of Socio-Cultural Information From Natural Language Conversations.” Pages 480-487 in Proceedings of the IEEE International Conference on Social Computing/IEEE International Conference on Privacy, Security, Risk and Trust. SIN- The Second International Symposium on Social Intelligence and Networking. Los Alimitos, California: IEEE Computer Society. 2010.

Scherl, R., Tran, C. and Baral, C. “State-Based Regression with Sensing and Knowledge.” International Journal of Software and Informatics. 3(1):3-30. 2009.

Gilett, P., Scherl, R. and Shafer, G. “A Probabilistic Logic based on the Acceptability of Gambles.” Pages 281-300. International Journal of Approximate Reasoning. Volume 44, Issue 3. March 2007.

Baral, C., Gelfond, G., Gelfond, M., and Scherl, R. “Textual Inference by Combining Multiple Logic Programming Paradigms.” Pages 1-5 in Proceedings of the AAAI-05 Workshop on Inference for Textual Question Answering. July 9, 2005, Pittsburgh, Pennsylvania.

Baral, C., Gelfond, M., and Scherl, R. “Answer Set Programming as the basis for a Homeland Security QAS.” Pages 149-150 in Proceedings of the 2005 AAAI Spring Symposium on AI Technologies for Homeland Security, Edited by John Yen and Robert Popp. March 21 – 23, 2005. Stanford, California.

Baral, C., Gelfond, M., and Scherl, R. “Using Answer Set Programming to Answer Complex Queries.” Pages 17-22 in the Workshop on Pragmatics of Question Answering at HLT-NAACL 2004 (Human Language Technology – North American Association for Computational Linguistics). Boston, Mass. May 2004.

Scherl, R. “Reasoning about the Interaction of Knowledge, Time and Concurrent Actions in the Situation Calculus.” Proceedings of the Eighteenth International Conference on Artificial Intelligence (IJCAI-03), pp. 1091-1096, Acapulco, Mexico, August 9-15, 2003.

Scherl, R. and Levesque, H. “Knowledge, Action, and the Frame Problem.” Artificial Intelligence, pp. 1-39, vol. 144, 2003.

Scherl, R. and Geller, J. “Global Communities, Marketing, and Web Mining.” Doing Business Across Borders, pp. 141-150, vol. 1, no. 2, November 2002.

Shafer, G, Gillett, P. and Scherl, R. “A New Understanding of Subjective Probability and its Generalization to Lower and Upper Prevision.” International Journal of Approximate Reasoning, pp. 1-49, vol. 33, Issue 1, 2003.

Professional Associations

American Association for Artificial Intelligence, Association for Computing Machinery

Courses

Recently Taught Classes

2024 Fall

  • Advanced Computing – CS 305
  • Introduction to Intelligent Systems – CS 520
  • Survey of Artificial Intelligence Concepts and Practices – CS 420

2024 Spring

  • Algorithm Design – CS 512
  • Computer Programming Essentials – CS 501A
  • Introduction to Computer Programming for Data Science I – DS 502
  • Introduction to Computer Science I – CS 175
  • Introduction to Computer Science II – CS 176
  • Program Development – CS 501B

2023 Fall

  • Advanced Computing – CS 305
  • Introduction to Intelligent Systems – CS 520
  • Survey of Artificial Intelligence Concepts and Practices – CS 420

2023 Spring

  • Algorithm Design – CS 512
  • Computer Programming Essentials – CS 501A
  • Introduction to Computer Programming for Data Science II – DS 503
  • Introduction to Computer Science I – CS 175
  • Introduction to Computer Science II – CS 176
  • Program Development – CS 501B

2022 Fall

  • Advanced Computing – CS 305
  • Computer Programming Essentials – CS 501A
  • Introduction to Computer Programming for Data Science I – DS 502
  • Introduction to Computer Science I – CS 175
  • Introduction to Intelligent Systems – CS 520
  • Survey of Artificial Intelligence Concepts and Practices – CS 420

2022 Spring

  • Algorithm Design – CS 512
  • Introduction to Computer Science II – CS 176
  • Program Development – CS 501B

2021 Fall

  • Advanced Computing – CS 305
  • Introduction to Intelligent Systems – CS 520
  • Survey of Artificial Intelligence Concepts and Practices – CS 420

2021 Spring

  • Advanced Special Topics – CS 698
  • Data Mining – CS 618
  • Introduction to Computer Science II – CS 176
  • Program Development – CS 501B

Frequently Taught Classes

  • Advanced Computing (CS 305)
  • Advanced Special Topics (CS 698)
  • Algorithm Design (CS 512)
  • Computer Algorithms I (CS 305)
  • Computer Programming Essentials (CS 501A)
  • Cooperative Education: Computer Science (CS 488)
  • Data Mining (CS 618)
  • Data Structures and Algorithms (CS 305)
  • Database Design and Management (CS 517, MIS 517)
  • Database Systems (CS 517)
  • Discrete Mathematics and Applications (CS 202)
  • Introduction to Computer Programming for Data Science I (DS 502)
  • Introduction to Computer Science I (CS 175)
  • Introduction to Computer Science II (CS 176)
  • Introduction to Computer Science II Lab (CS 176L)
  • Introduction to Intelligent Systems (CS 520)
  • Issues in Cognitive Science (PR 457)
  • Program Development (CS 501B)
  • Survey of Artificial Intelligence Concepts and Practices (CS 420)
  • Theoretical Foundations of Computer Science (CS 502)