Common Sense Reasoning
Section
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Course description
General description of this course
A fundamental challenge in artificial intelligence is the construction of machines capable of reasoning with common sense. Common sense reasoning methods can help machines make more robust decisions, based on consistent assumptions about the real world, and can significantly simplify human-machine communication.
This challenge is one of the most difficult problems in building machines with human level intelligence. The scientific community in artificial intelligence has proposed for several decades partial solutions to build machines with common sense (e.g., considering extensions of classical first order logic, as well as other types of approaches, such as qualitative representations, etc.). The recent technological advances, such as big data, machine learning, natural language processing, etc., have facilitated the proposal of new methods of representation and extraction of common sense knowledge.
The goal of this course is to present the main areas of common sense reasoning with special attention to the recent advances of this field within artificial intelligence. First, the course describes inference methods and algorithms that simulate common sense reasoning (logic-based methods, physical reasoning, etc.). Then, the course describes how to build common sense knowledge bases, reviewing different approaches that cover both manual and automatic methods. Finally, the course presents applications of common sense reasoning in areas such as question-answering systems, natural language understanding, etc.
Learning outcomes
This course is aimed at postgraduates and researchers in computer science who want to get a comprehensive understanding of artificial intelligence and its fundamental problems and solutions in the particular field of common sense reasoning. As learning outcomes of this course, students will be able to:
1. List the main challenges of common sense reasoning in artificial intelligence.
2. Describe both theoretical and practical achievements of common sense reasoning in artificial intelligence.
3. Explain the current scientific and technological limitations of simulating common sense reasoning.
4. Recognize the main contributors (e.g., scientists) and research centers in the area of common sense reasoning.
5. Formulate areas of applications of common sense reasoning.
6. Find specialized bibliography about common sense reasoning.
Course content
Part I: Introduction
1. Introduction to common sense reasoning
Part II: Common sense reasoning methods
2. Simulating common sense reasoning
3. Event calculus
4. Physical reasoning
5. Temporal and spatial reasoning
Part III: Common sense knowledge bases
6. Building common sense knowledge bases
7. Manual acquisition of common sense knowledge bases
8. Collective acquisition of common sense knowledge bases
9. Automatic generation of large scale data bases
10. Learning common sense knowledge
11. Integrating common sense knowledge
Part IV: Applications
12. Using natural language to access to data
13. Challenges in natural language understanding
14. Understanding user intentions
15. Other applications of common sense reasoning
Requirements and prior knowledge
This material of this course was created to be used in the postgraduate master’s degree in Artificial Intelligence (Universidad Politécnica de Madrid). It is assumed that students are familiar with general methods of computer science (e.g., formalization of computer algorithms) and basic concepts about artificial intelligence (e.g., knowledge representation).
Teaching material
The material of this course includes mainly sets of slides and selected publications (books and scientific papers) that cover relevant content related to common sense reasoning. Slides have references to publications where students can find more detailed information.
Cite this course
This course may be cited using the following format:
Molina, M. (2019). Common sense reasoning [Lecture slides]. OpenCourseWare, Universidad Politécnica de Madrid. Retrieved from http://ocw.upm.es/course
Course schedule
Class Time Units Lecture slides Suggested readings Recommended books Suggested Links 1 1h Course presentation Unit 1. Introduction to common sense reasoning S-01 B-01 L-01, L-02, L-03 2 1h Unit 2. Simulating common sense reasoning S-02 R-01 L-04 Unit 3. Event calculus S-03 B-02 Unit 4. Physical reasoning S-04 R-02, R-03 B-03 3 1h Unit 5. Temporal and spatial reasoning S-05 R-04 4 1h Unit 6. Building common sense knowledge bases S-06 Unit 7. Manual acquisition of common sense knowledge S-07 R-05 B-04 L-05, L-06 5 1h Unit 8. Collective acquisition of common sense knowledge S-08 R-06 6 1h Unit 9. Automatic generation of large scale data bases S-09 R-07, R-08 7 1h Unit 10. Learning common sense knowledge S-10 Unit 11. Integrating common sense knowledge S-11 R-09 8 1h Unit 12. Using natural language to access to data S-12 R-10 Unit 13. Challenges in natural language understanding S-13 R-11 B-05 9 1h Unit 14. Understanding user intentions S-14 R-12, R-13 Unit 15. Other applications of common sense reasoning S-15 B-06 L-07, L-08 10 1h Final written examination
Lecture slides
- References (PDF)
- S-01. Introduction to common sense reasoning (PDF)
- S-02. Simulating common sense reasoning (PDF)
- S-03. Event calculus (PDF)
- S-04. Physical reasoning (PDF)
- S-05. Temporal and spational reasoning (PDF)
- S-06. Building common sense knowledge bases (PDF)
- S-07. Manual acquisition of common sense knowledge (PDF)
- S-08. Collective acquisition of common sense knowledge (PDF)
- S-09. Automatic generation of large scale data bases (PDF)
- S-10. Learning common sense knowledge (PDF)
- S-11. Integrating common sense knowledge (PDF)
- S-12. Using natural language to access to data (PDF)
- S-13. Challenges in natural language understanding (PDF)
- S-14. Understanding user intentions (PDF)
- S-15. Other applications of common sense reasoning (PDF)
Recommended books
B-01. Minsky, M. (2006): “The emotion machine”. Simon & Schuster.
B-02. Mueller, E. (2014): “Commonsense reasoning: an event calculus based approach” (2nd Edition). Morgan Kaufmann.
B-03. Davis, E. (1990): “Representations of Commonsense Knowledge” Morgan Kaufmann.
B-04. Lenat, D., Guha, R. V (1990): “Building Large Knowledge Bases”. Addison-Wesley, Reading, Mass., 1990.
B-05. Ovchinnikova, E. (2012): “Integration of world knowledge for natural language understanding” (Vol. 3). Springer Science & Business Media.
B-06. Johnston, B. (2009): “Practical Artificial Commonsense”. University of Technology, Sydney PhD thesis.
Suggested readings
R-01. McCarthy, J. (1980): “Circumscription - A form of non-monotonic reasoning”. Artificial intelligence, 13(1-2), 27-39.
R-02. Hayes, P. (1978): “The Naive Physics Manifesto”. In: D. Michie (Ed.), Expert Systems in the Microelectronic Age. Edinburgh, Scotland: Edinburgh University Press, pp. 242-270
R-03. Forbus, K.D. (2008): “Qualitative Modeling”. In F. v. Harmelen, V. Lifschitz and B. Porter (eds), Handbook of Knowledge Representation (Volume 3), pages 361- 393.
R-04. Cohn, A. G., Renz, J. (2008): “Qualitative spatial representation and reasoning”. Foundations of Artificial Intelligence, 3, 551-596.
R-05. Pease, A., Niles, I., Li, J. (2002): “The suggested upper merged ontology: A large ontology for the semantic web and its applications”. In Working notes of the AAAI-2002 workshop on ontologies and the semantic web (Vol. 28, pp. 7-10).
R-06. Singh, P., Lin, T., Mueller, E., Lim, G., Perkins, T., Zhu W.L. (2002): “Open Mind Common Sense: Knowledge acquisition from the general public”. Proceedings of the First International Conference on Ontologies, Databases, and Applications of Semantics for Large Scale Information Systems. Irvine, CA.
R-07. Suchanek, F. M., Kasneci, G., Weikum, G. (2007): “Yago: a core of semantic knowledge”. In Proceedings of the 16th international conference on World Wide Web (pp. 697-706). ACM.
R-08. Mitchell, T., Cohen, W., Hruschka, E., Talukdar, P., Yang, B., Betteridge, J., et al. (2015): “Never-ending learning”. In Proceedings of the Conference on Artificial Intelligence (AAAI), 2015
R-09. Speer, R., Chin, J., Havasi, C. (2017): “ConceptNet 5.5: An open multilingual graph of general knowledge”. In Thirty-First AAAI Conference on Artificial Intelligence.
R-10. Ferrucci, D., Brown, E., Chu-Carroll, J., Fan, J., Gondek, D., Kalyanpur, A. A., et al. (2010): “Building Watson: An overview of the DeepQA project”. AI magazine, 31(3), 59-79.
R-11. Levesque, H., Davis, E., Morgenstern, L. (2011): “The Winograd Schema challenge”. AAAI Spring Symposium: Logical Formalizations of Commonsense Reasoning.
R-12. Minsky, M. (2000): “Commonsense-based interfaces”, Communications of the ACM 43(8): 67-73. ACM Press.
R-13. Lieberman, H., Liu, H., Singh, P., Barry, B. (2004): “Beating common sense into interactive applications”. AI Magazine 25(4): 63-76. AAAI Press.
Suggested links
- L-01. Davis, E. (2012): “Why are computers so stupid and what can be done about it” [video]. http://www.pppl.gov/events/why-are-computers-so-stupid-and-what-can-be-done-about-it-prof-ernest-davis-computer-science
- L-02. Lenat, D. (2015): “Computers with common sense” [video].
- L-03. Davis, E., Marcus, G. (2015): “Commonsense reasoning and commonsense knowledge in artificial intelligence” [video]. https://vimeo.com/134965536
- L-04. Tenenbaum, J. (2018): “Building machines that see, learn, and think like people” [video]
- L-05. Lenat, D. (2006): “Computers versus common sense” [video]
- L-06. Pease, A. (2011): “Formal Ontology and the Suggested Upper Merged Ontology (SUMO)” [video].
- L-07. Lenat, D. (2015): “50 Shades of Symbolic Representation and Reasoning” [video].
- L-08. Knight, W. (2017): “Finally a driverless car with some common sense” https://www.technologyreview.com/s/608871/finally-a-driverless-car-with-some-common-sense/
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