Skip to main content
SpringerLink
Log in

Introduction to Logic-Based Artificial Intelligence

  • Chapter
Book cover Logic-Based Artificial Intelligence

Part of the book series: The Springer International Series in Engineering and Computer Science ((SECS,volume 597))

Abstract

In this chapter I provide a brief introduction to the field of Logic-Based Artificial Intelligence (LBAI). I then discuss contributions to LBAI contained in the chapters and some of the highlights that took place at the Workshop on LBAI from which the papers are drawn. The areas of LBAI represented in the book are: commonsense reasoning; knowledge representation; nonmonotonic reasoning; abductive and inductive reasoning; logic, probability and decision making; logic for causation and actions; planning and problem solving; logic, planning and high-level robotics; logic for agents and actions; theory of beliefs; logic and language; computational logic; system implementations; and logic applications to mechanical checking and data integration.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Allen, J. (1984). Towards a general theory of action and time. Artificial Intelligence, 23:123–154.

    Article  MATH  Google Scholar 

  • Apt, K., Blair, H., and Walker, A. (1988). Towards a theory of declarative knowledge. In Minker, J., editor, Foundations of Deductive Databases and Logic Programming, pages 89–148. Morgan Kaufmann Pub., Los Altos, CA.

    Google Scholar 

  • Apt, K. R. and van Emden (1982) Contributions to the theory of logic programming J. ACM, 29(3):841–862.

    Article  MATH  Google Scholar 

  • Bacchus, F., Grove, A., Halpern, J., and Koller, D. (1993). Statistical foundations for default reasoning. Proc. IJCAI-93, pages 563–569.

    Google Scholar 

  • Bacchus, F. (1990) Representing and Reasoning with Probabilistic Knowledge. MIT Press.

    Google Scholar 

  • Bacchus, F. and Kabanza, F. (2000). Using temporal logics to express search control knowledge for planning. Artificial Intelligence, 16:123–191.

    Article  MathSciNet  Google Scholar 

  • Baral, C. and Gelfond, M. (1994). Logic programming and knowledge representation. Journal of Logic Programming, 19/20:73–148.

    Article  MathSciNet  Google Scholar 

  • Boutilier, C., Reiter, R., Soutchanski, M., and Thrun, S. (2000). Decision-theoretic, high level agent programming in the situation calculus. In Proc. Amer. Assoc. for Artificial Intelligence — 2000.

    Google Scholar 

  • Boyer, R. S. and Moore, J. S. (1979). A Computational Logic. Academic Press.

    Google Scholar 

  • Boyer, R. S. and Moore, J. S. (1997). A Computational Logic Handbook, Second Edition. Academic Press.

    Google Scholar 

  • Brass, S., Dix, J., and Przymusinski, T. C. (1996). Super logic programs. Knowledge Representation, pages 529–540.

    Google Scholar 

  • Brooks, R. A. (1991). Intelligence without reason. pages 569–595. Morgan Kaufmann.

    Google Scholar 

  • Cadoli, M. and Lenzerini, M. (1991). The complexity of closed world reasoning and circumscription. Knowledge Representation, pages 550–555.

    Google Scholar 

  • Cadoli, M. and Schaerf, M. (1992). A survey on complexity results for nonmonotonic logics. Technical report, University di Roma “La Sapienza”, Dipartiment o di Informatica e sistemistica, Roma, Italy.

    Google Scholar 

  • Chandra, A. and Harel, D. (1985). Horn clause queries and generalizations. Journal of Logic Programming, 2(1):1–15.

    Article  MathSciNet  MATH  Google Scholar 

  • Chang, C. L. and Lee, R. C. T. (1973). Symbolic Logic and Mechanical Theorem Proving. Academic Press, New York.

    MATH  Google Scholar 

  • Cholewiński, P., Marek, V. W. and Mikitiuk, A., and Truszczyński, M. (1999). Computing with default logic. Artificial Intelligence, 112.

    Google Scholar 

  • Cholewiński, P., Marek, W., and Truszczyński, M. (1996). Default reasoning system deres. In Proceedings of KR-96, pages 518–528, San Francisco, California. Morgan Kaufmann.

    Google Scholar 

  • Clark, K. L. (1978). Negation as Failure. In Gallaire, H. and Minker, J., editors, Logic and Data Bases, pages 293–322. Plenum Press, New York.

    Chapter  Google Scholar 

  • Colmerauer, A. (1985). Prolog in 10 figures. Communications of the ACM, 28(12):1296–1310.

    Article  MathSciNet  Google Scholar 

  • Colmerauer, A., Kanoui, H., Pasero, R., and Roussel, P. (1973). Un systeme de communication homme-machine en francais. TR, Groupe de Intelligence Artificielle Univ. de Aix-Marseille II, Marseille.

    Google Scholar 

  • Dantsin, E., Eiter, T., Gottlob, G., and Voronkov, A. (1997). Complexity and expressive power of logic programming. In Proc. of 12th annual IEEE Conference on Computational Complexity, pages 82–101.

    Google Scholar 

  • Davis, E. (1998). The naive physics perplex. AI Magazine, 19(14):51–79.

    Google Scholar 

  • Davis, E. (1999). Guide to axiomatizing domains in first-order logic. Electronic Newsletter on Reasoning About Actions and Change.

    Google Scholar 

  • Davis, M. and Putnam, H. (1960). A computing procedure for quantification theory. J. ACM, 7:201–215.

    Article  MathSciNet  MATH  Google Scholar 

  • Dimopoulos, Y. and Kakas, A. (1995). Abduction and inductive learning. In De Raedt, L., editor, Proc. 5th Inductive Logic Programming Workshop (ILP95), pages 25–28, Leuven, Belgium. KU Leuven.

    Google Scholar 

  • Eiter, T., Leone, N., Mateis, C., Pfeifer, G., and Scarcello, F. (1997). A deductive system for nonmonotonic reasoning. In Dix, J., Furbach, U., and Nerode, A., editors, Proc. 4th Int’l. Conf. on Logic Programming and Nonmonotonic Reasoning, number 1265 in Lecture Notes in AI. Springer.

    Google Scholar 

  • Fikes, R. and Nilsson, N. (1971). STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving. Artificial Intelligence, 5(2): 189–208.

    Article  Google Scholar 

  • Fikes, R. and Nilsson, N. (1993). STRIPS, a retrospective. Journal of Artificial Intelligence, 59(1/2):227–232.

    Article  Google Scholar 

  • Fitting, M. C. (2000). Fixpoint semantics for logic programming — a survey. Theoretical Computer Science. To appear.

    Google Scholar 

  • Gallaire, H. and Minker, J., editors (1978). Logic and Databases. Plenum Press, New York.

    Google Scholar 

  • Gallaire, H., Minker, J., and Nicolas, J.-M. (1984). Logic and databases: A deductive approach. ACM Computing Surveys, 16(2):153–185.

    Article  MathSciNet  MATH  Google Scholar 

  • Geffner, H. (1990). Causal theories for nonmonotonic reasoning. In Proc. AAAI-90, pages 524–530. AAAI Press.

    Google Scholar 

  • Gelfond, M. and Lifschitz, V. (1988). The stable model semantics for logic programming. In Kowalski, R. and Bowen, K., editors, Logic Programming: Proc. 5th Int’l Conf. and Symp., pages 1070–1080.

    Google Scholar 

  • Gelfond, M. and Lifschitz, V. (1990). Logic programs with classical negation. In Warren, D. and Szeredi, P., editors, Proc. 7th Int’l Conf. on Logic Programming, pages 579–597, Jerusalem, Israel. MIT Press.

    Google Scholar 

  • Gelfond, M. and Lifschitz, V. (1992). Representing actions in extended logic programming. In Apt, K., editor, Proc. Joint Int’l Conf. and Symp. on Logic Programming, pages 559–573.

    Google Scholar 

  • Gelfond, M. and Lifschitz, V. (1993). Representing actions and change by logic programs. Journal of Logic Programming, 17(2,3,4):301–323.

    Article  MathSciNet  MATH  Google Scholar 

  • Gelfond, M. and Lifschitz, V. (1998). Action languages. Electronic Transactions on AI, 3. (http://www.ep.liu.se/ea/cis/1998/016)

  • Gelfond, M., Lifschitz, V., and Rabinov, A. (1991). What are the limitations of the situation calculus? In Boyer, R., editor, Automated Reasoning: Essays in Honor of Woody Bledsoe, pages 167–179. Kluwer.

    Google Scholar 

  • Ginsberg, M. and Smith, D. (1988a). Reasoning about action I: a possible world approach. Artificial Intelligence, 35:165–195.

    Article  MathSciNet  MATH  Google Scholar 

  • Ginsberg, M. and Smith, D. (1988b). Reasoning about action II: the qualification problem. Artificial Intelligence, 35:311–342.

    Article  MathSciNet  MATH  Google Scholar 

  • Giunchiglia, E. and Lifschitz, V. (1998). An action language based on causal explanation: Prelim. Rpt. In Proc. AAAI-98, pages 623–630. AAAI Press.

    Google Scholar 

  • Green, C. (1969). Theorem proving by resolution as a basis for question — answering systems. In Michie, B. M. D., editor, Machine Intelligence 4, pages 183–205. Edinburgh University Press, New York.

    Google Scholar 

  • Green, C. and Raphael, B. (1968a). Research in intelligent question answering systems. Proc. ACM 23rd National Conf., pages 169–181.

    Google Scholar 

  • Green, C. and Raphael, B. (1968b). The use of theorem-proving techniques in question-answering systems. Proc. ACM 23rd National Conf..

    Google Scholar 

  • Haas, A. (1987). The case for domain-specific frame axioms. In Brown, F. M., editor, The Frame Problem in Artificial Intelligence, (Proc. 1987 Workshop).

    Google Scholar 

  • Hanks, S. and McDermott, D. (1987). Nonmonotonic logic and temporal projection. Artificial Intelligence, 33(3):379–412.

    Article  MathSciNet  MATH  Google Scholar 

  • Hayes, P. (1973a). Computation and deduction. In Proceedings of the 2nd Symp. on Mathematical Foundations of Computer Science, pages 107–113, Czechoslovakia: Czechoslovakian Academy of Sciences.

    Google Scholar 

  • Hayes, P. (1985a). Naive physics i: ontology for liquids. In Hobbs, J. and Moore, R., editors, Formal Theories of the Commonsense World, chapter 3, pages 71–107. Ablex, Norwood, New Jersey.

    Google Scholar 

  • Hayes, P. (1985b). The second naive physics manifesto. In Hobbs, J. and Moore, R., editors, Formal Theories of the Commonsense World, chapter 1, pages 1–36. Ablex, Norwood, New Jersey.

    Google Scholar 

  • Hayes, P. J. (1973b). The frame problem and related problems in artificial intelligence. Artificial and Human Thinking, pages 45–59.

    Google Scholar 

  • Jaffar, J. and Maher, M. (1994). Constraint logic programming: a survey. Journal of Logic Programming, 19–20:503–581.

    Article  MathSciNet  Google Scholar 

  • Jenkin, M., Lespérance, Y., Levesque, H., Lin, F., Lloyd, J., Marcu, D., Reiter, R., Scherl, R., and Tam, K. (1997). A logical approach to portable high-level robot programming. In Proc. 10th Australian Joint Conf. on Artificial Intelligence (AI’97), Perth, Australia.

    Google Scholar 

  • Jennings, N. R., Sycara, K., and Wooldridge, M. (1998). A roadmap of agent research and development. Autonomous Agents and Multi-Agent Systems, 1:7–38.

    Article  Google Scholar 

  • Kakas, A. C., Kowalski, R. A., and Toni, F. (1993). Abductive logic programming. Journal of Logic and Computation, 6(2):719–770.

    MathSciNet  Google Scholar 

  • Kaufmann, M. and Moore, J. S. (1997). An industrial strength theorem prover for a logic based on Common Lisp. IEEE Transactions on Software Engineering, 23(4):203–213.

    Article  Google Scholar 

  • Kaufmann, M. and Moore, J. S. (1999). The ACL2 user’s manual. http://www.cs.utexas.edu/users/moore/acl2/acl2-doc.html.

  • Kautz, H. and Selman, B. (1999b). Unifying sat-based and graph-based planning. In Proc. of IJCAI 99, pages 318–325.

    Google Scholar 

  • Kowalski, R. (1974). Predicate logic as a programming language. Proc. IFIP 4, pages 569–574.

    Google Scholar 

  • Lespérance, Y., Levesque, H., Lin, F., Marcu, D., Reiter, R., and Scherl, R. (1994). A logical approach to high-level robot programming — a progress report. In Control of the Physical World by Intelligent Systems, Working Notes of the 1994 AAAI Fall Symp.

    Google Scholar 

  • Lespérance, Y., Levesque, H. J., Lin, F., Marcu, D., Reiter, R., and Scherl, R. B. (1994). A logical approach to high-level robot programming: A progress report. In B. Kuipers, editor, Control of the Physical World by Intelligent Systems: Papers from 1994 AAAI Fall Symp..

    Google Scholar 

  • Levesque, H., Reiter, R., Lespérance, Y., Lin, F., and Scherl, R. (1997). GOLOG: a logic programming language for dynamic domains. J. of Logic Programming, Special Issue on Actions, 31(1–3):59–83.

    MATH  Google Scholar 

  • Lifschitz, V. (1987). On the semantics of STRIPS. In Georgeff, M. and Lansky, A., editors, Reasoning about Actions and Plans, pages 1–9. Morgan Kaufmann, Morgan Kaufmann, CA.

    Chapter  Google Scholar 

  • Lin, F. (1995). Embracing causality in specifying the indirect effects of actions. In Proc. IJCAI-95, pages 1985–1991.

    Google Scholar 

  • Lobo, J., Minker, J., and Rajasekar, A. (1992). Foundations of Disjunctive Logic Programming. MIT Press.

    Google Scholar 

  • Loveland, D. (1978). Automated Theorem Proving: A Logical Basis. North-Holland Publishing Co.

    Google Scholar 

  • Loveland, D. (1999). Automated deduction: Looking ahead. AI Magazine, 20(l):77–98.

    Google Scholar 

  • Marek, V. and Truszczyński, M. (1993). Nonmonotonic Logic: Context-Dependent Reasoning. Springer-Verlag.

    Google Scholar 

  • McCain, N. and Turner, H. (1997). Causal theories of action and change. In Proc. AAAI-97, pages 460–465.

    Google Scholar 

  • McCarthy, J. (1959). Programs with commonsense. In Proc. Teddington Conf. on the Mechanisation of Thought Processes, pages 75–91, London. Her Majesty’s Stationery Office. Reprinted (with an added section on ‘Situations, Actions and Causal Laws’) in Semantic Information Processing, ed. M. Minsky (Cambridge, MA: MIT Press (1963)).

    Google Scholar 

  • McCarthy, J. (1963). Situations, actions and causal laws. Memo 2. AI Laboratory, Stanford University, Stanford, CA.

    Google Scholar 

  • McCarthy, J. (1977). Epistemological problems in artificial intelligence. In Proc. 5th International Conference on Artificial Intelligence, pages 1038–1044.

    Google Scholar 

  • McCarthy, J. (1978). History of lisp. In Wexblatt, R., editor, History of Programming Languages: Proc. of the ACM SIGPLAN Conf., pages 3–57. Academic Press. Published in 1981 (Conf. date: 1978).

    Google Scholar 

  • McCarthy, J. (1980). Circumscription — a form of non-monotonic reasoning. Artificial Intelligence, 13(1 and 2):27–39.

    Article  MathSciNet  MATH  Google Scholar 

  • McCarthy, J. and Hayes, P. (1969b). Some philosophical problems from the standpoint of artificial intelligence. In Meltzer, B. and Michie, D., editors, Machine Intelligence 4, pages 463–502. Edinburgh University Press.

    Google Scholar 

  • McCune, W. (1997). Solution of the Robbins problem. J. Automated Reasoning, 19(3):263–276.

    Article  MathSciNet  MATH  Google Scholar 

  • Minker, J. (1988). Perspectives in deductive databases. Journal of Logic Programming, 5:33–60.

    Article  MathSciNet  Google Scholar 

  • Minker, J. (1993). An overview of nonmonotonic reasoning and logic programming. Journal of Logic Programming, 17(2, 3 and 4):95–126.

    Article  MathSciNet  MATH  Google Scholar 

  • Minker, J. (1994). Overview of disjunctive logic programming. Journal of Artificial Intelligence & Mathematics, 12(1–2): 1–24.

    Article  MathSciNet  Google Scholar 

  • Minker, J. (1996). Logic and databases: a 20 year retrospective. In Pedreschi, D. and Zaniolo, C., editors, Logic in Databases, pages 3–57. Springer. Proc. Int. Workshop LID’96, San Miniato, Italy.

    Google Scholar 

  • Minker, J. (1999a). Logic and databases: a 20 year retrospective — updated in honor of Ray Reiter. In Levesque, H. J. and Pirri, F., editors, Logical Foundations for Cognitive Agents: Contributions in Honor of Ray Reiter, pages 234–299. Springer.

    Google Scholar 

  • Minker, J. (1999b). The workshop on logic-based artificial intelligence. AI Magazine, 20(4):21–47.

    Google Scholar 

  • Minsky, M. (1975). A framework for representing knowledge. In Winston, P., editor, The Psych. of Computer Vision, pages 211–277. McGraw-Hill, NY.

    Google Scholar 

  • Moore, R. C. (1985). Semantical considerations on nonmonotonic logic. Artificial Intelligence, 25(l):75–94.

    Article  MathSciNet  MATH  Google Scholar 

  • Niemela, I. and Simons, P. (1997). Smodels — an implementation of the stable model and well-founded semantics for normal logic programs. In Dix, J., Furbach, U., and Nerode, A., editors, Logic Programming and Nonmonotonic Reasoning — 4th Int. Conf., pages 420–429, Dagstuhl, Germany. Springer.

    Chapter  Google Scholar 

  • Nilsson, N. (1982). Principles of Artificial Intelligence. Springer-Verlag.

    Google Scholar 

  • Nilsson, N. (1984). Shakey the robot. Technical Note 323, SRI International, Menlo Park, California.

    Google Scholar 

  • Pednault, E. (1989). ADL: Exploring the middle ground between STRIPS and the situation calculus. In Brachman, R., Levesque, H., and Reiter, R., editors, Proc. First Int%l Conf. on Principles of Knowledge Representation and Reasoning, pages 324–332.

    Google Scholar 

  • Peirce, C. S. (1883). A theory of probable inference. note b. the logic of relatives. In Studies in logic by members of the Johns Hopkins Univ., pages 187–203.

    Chapter  Google Scholar 

  • Plotkin, G. (1969). A note on inductive generalisation. In Meltzer, B. and Michie, D., editors, Machine Intelligence 5, pages 153–163. Edinburgh University Press, Edinburgh.

    Google Scholar 

  • Plotkin, G. (1971). Automatic Methods of Inductive Inference. PhD thesis, Edinburgh University.

    Google Scholar 

  • Przymusinski, T. C. (1988). On the declarative semantics of deductive databases and logic programming. In Minker, J., editor, Foundations of Deductive Databases and Logic Programming, chapter 5, pages 193–216. Morgan Kaufmann Pub., Washington, D.C.

    Google Scholar 

  • Quillian, R. (1968). Semantic Memory. In Minsky, M., editor, Semantic Information Processing, pages 216–270. MIT Press, Cambridge, Massachusetts.

    Google Scholar 

  • Rao, P., Sagonas, K., Swift, T., Warren, D., and Friere, J. (1997). XSB: A system for efficiently computing well-founded semantics. In Dix, J., Ferbach, U., and Nerode, A., editors, Logic and Nonmonotonic Reasoning — 4t h Int’l. Conf., LPNMR’97, pages 430-440.

    Chapter  Google Scholar 

  • Reiter, R. (1980). A Logic for Default Reasoning. Artificial Intelligence, 13(1 and 2):81–132.

    Article  MathSciNet  MATH  Google Scholar 

  • Reiter, R. (1991). The frame problem in the situation calculus: A simple solution (sometimes) and a completeness result for goal regression. In Lifschitz, V., editor, AI and Mathematical Theory of Computation: Papers in Honor of John McCarthy, pages 359–380. Academic Press.

    Google Scholar 

  • Reiter, R. (1993). Proving properties of states in the situation calculus. Artificial Intelligence, 64:337–351.

    Article  MathSciNet  MATH  Google Scholar 

  • Robinson, J. A. (1965). A machine oriented logic based on the resolution principle. Journal of the ACM, 12:23–41.

    Article  MATH  Google Scholar 

  • Russell, S. and Norvig, P. (1995). Artificial Intelligence: A Modern Approach. Prentice Hall.

    Google Scholar 

  • Sandewall, E. (1995). Features and Fluents, volume 1. Oxford University Press.

    Google Scholar 

  • Schank, R. and Abelson, R. (1977). Scripts, Plans, Goals, and Understanding. Lawrence Erlebaum.

    Google Scholar 

  • Schubert, L. (1990). Monotonic solution of the frame problem in the situation calculus: an efficient method for worlds with fully specified actions. In Kyburg, H., Loui, R., and Carlson, G., editors, Knowledge Representation and Defeasible Reasoning, pages 23–67. Kluwer.

    Google Scholar 

  • Shanahan, M. P. (1997). Solving the Frame Problem: A Mathematical Investigation of the Common Sense Law of Inertia. MIT Press.

    Google Scholar 

  • van Emden, M. and Kowalski, R. (1976). The Semantics of Predicate Logic as a Programming Language. J. ACM, 23(4):733-742.

    Article  MATH  Google Scholar 

  • Van Gelder, A. (1988). Negation as failure using tight derivations for general logic programs. In Minker, J., editor, Found. of Deductive Databases and Logic Programming, pages 149–176. Morgan Kaufmann.

    Google Scholar 

  • Van Gelder, A., Ross, K., and Schlipf, J. (1988). Unfounded sets and well-founded semantics for general logic programs. In Proc. 7th ACM Symp. on Principles of Database Systems., pages 221–230.

    Google Scholar 

  • Warren, D. S. (1999). The XSB programming system. Technical report, State University of New York at Stonybrook. http://www. cs.sunysb.edu/sbprolog/xsb-page.html.

  • Wooldridge, M. and Jennings, N. (1995). Intelligent agents: Theory and practice. The Knowledge Engineering Review, 10(2): 115–152.

    Article  Google Scholar 

  • Zaniolo, C., Arni, N., and Ong, K-L. (1993). Negation and aggregates in recursive rules. Proceedings of DOOD93, pages 204–221.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Institute for Advanced Computer Studies, University of Maryland, College Park, Maryland, 20742, USA

    Jack Minker

Authors
  1. Jack Minker
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute for Advanced Computer Studies and Department of Computer Science, University of Maryland, USA

    Jack Minker

Rights and permissions

Reprints and permissions

Copyright information

© 2000 Springer Science+Business Media New York

About this chapter

Cite this chapter

Minker, J. (2000). Introduction to Logic-Based Artificial Intelligence. In: Minker, J. (eds) Logic-Based Artificial Intelligence. The Springer International Series in Engineering and Computer Science, vol 597. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1567-8_1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4615-1567-8_1

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-5618-9

  • Online ISBN: 978-1-4615-1567-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation