Allemang, D., and Hendler, J. (2011). Semantic Web for the Working Ontologist: Effective Modeling in RDFS and Owl, Morgan Kaufmann, San Mateo, CA.

Allen, J., Hendler, J., and Tate, A., (eds.) (1990). Readings in Planning, Morgan Kaufmann, San Mateo, CA.

Anderson, T., Schum, D., and Twining, W. (2005). Analysis of Evidence, Cambridge University Press, Cambridge, UK.

Awad, E. M. (1996). Building Expert Systems: Principles, Procedures, and Applications, West, New York, NY.

Awad, E. M., and Ghaziri, H. M. (2004). Knowledge Management, Pearson Education International, Prentice Hall, Upper Saddle River, NJ, pp. 60–65.

Basic Formal Ontology (BFO) (2012). Basic Formal Ontology. www.ifomis.org/bfo (accessed August 31, 2012).

Betham, J. (1810). An Introductory View of the Rationale of the Law of Evidence for Use by Non-lawyers as Well as Lawyers (vi works 1–187 (Bowring edition, 1837–43, originally edited by Mill, James circa 1810).

Boicu, C. (2006). An Integrated Approach to Rule Refinement for Instructable Knowledge-Based Agents. PhD Thesis in Computer Science, Learning Agents Center, Volgenau School of Information Technology and Engineering, George Mason University, Fairfax, VA.

Boicu, C., Tecuci, G., and Boicu, M. (2005). Improving Agent Learning through Rule Analysis, in Proceedings of the International Conference on Artificial Intelligence, ICAI-05, Las Vegas, NV, June 27–30. lac.gmu.edu/publications/data/2005/ICAI3196Boicu.pdf (accessed April 12, 2016)

Boicu, M. (2002). Modeling and Learning with Incomplete Knowledge, PhD Dissertation in Information Technology, Learning Agents Laboratory, School of Information Technology and Engineering, George Mason University. lac.gmu.edu/publications/2002/BoicuM_PhD_Thesis.pdf (accessed November 25, 2015)

Boicu, M., Tecuci, G., Bowman, M., Marcu, D., Lee, S. W., and Wright, K. (1999). A Problem-Oriented Approach to Ontology Creation and Maintenance, in Proceedings of the Sixteenth National Conference on Artificial Intelligence Workshop on Ontology Management, July 18–19, Orlando, Florida, AAAI Press, Menlo Park, CA. lac.gmu.edu/publications/data/1999/ontology-1999.pdf (accessed November 25, 2015)

Boicu, M., Tecuci, G., Marcu, D., Bowman, M., Shyr, P., Ciucu, F., and Levcovici, C. (2000). Disciple-COA: From Agent Programming to Agent Teaching, in Proceedings of the Seventeenth International Conference on Machine Learning (ICML), Stanford, CA, Morgan Kaufman, San Mateo, CA, lac.gmu.edu/publications/data/2000/2000_il-final.pdf (accessed November 25, 2015)

Bowman, M. (2002). A Methodology for Modeling Expert Knowledge That Supports Teaching Based Development of Agents, PhD Dissertation in Information Technology, George Mason University, Fairfax, VA. lac.gmu.edu/publications/data/2002/Michael%20Bowman-Thesis.pdf (accessed November 25, 2015)

Bresina, J. L., and Morris, P. H. (2007). Mixed-Initiative Planning in Space Mission Operations, AI Magazine, vol. 28, no. 1, pp. 75–88.Google Scholar

Breuker, J., and Wielinga, B. (1989). Models of Expertise in Knowledge Acquisition, in Guida, G., and Tasso, C. (eds.), Topics in Expert Systems Design, Methodologies, and Tools, North Holland, Amsterdam, Netherlands, pp. 265–295.

Buchanan, B. G., and Feigenbaum, E. A. (1978). DENDRAL and META-DENDRAL: Their Applications Dimensions, Artificial Intelligence, vol. 11, pp. 5–24.Google Scholar

Buchanan, B. G., and Shortliffe, E. H. (eds.) (1984). Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project, Addison-Wesley, Reading, MA.

Buchanan, B. G., and Wilkins, D. C. (eds.) (1993). Readings in Knowledge Acquisition and Learning: Automating the Construction and Improvement of Expert Systems, Morgan Kaufmann, San Mateo, CA.

Buchanan, B. G., Barstow, D., Bechtal, R., Bennett, J., Clancey, W., Kulikowski, C., Mitchell, T., and Waterman, D. A. (1983). Constructing an Expert System, in Hayes-Roth, F., Waterman, D., and Lenat, D. (eds.), Building Expert Systems, Addison-Wesley, Reading, MA, pp. 127–168.

Carbonell, J. G. (1983). Learning by Analogy: Formulating and Generalizing Plans from Past Experience, in Michalski, R. S., Carbonell, J. M., and Mitchell, T. M., Machine Learning: An Artificial Intelligence Approach, Tioga, Wellsboro, PA, pp. 137–162.

Carbonell, J. G. (1986). Derivational Analogy: A Theory of Reconstructive Problem-Solving and Expertise Acquisition, in Michalski, R. S., Carbonell, J. G., and Mitchell, T. M. (eds.), Machine Learning: An Artificial Intelligence Approach, vol. 2, Morgan Kaufmann, San Mateo, CA, pp. 371–392.

Chaudhri, V. K., Farquhar, A., Fikes, R., Park, P. D., and Rice, J. P. (1998). OKBC: A Programmatic Foundation for Knowledge Base Interoperability, in Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98), AAAI Press, Menlo Park, CA, pp. 600–607.

Clancey, W. (1985). Heuristic Classification, AI Journal, vol. 27, pp. 289–350.Google Scholar

Clausewitz, C. (1832 [1976]). On War, translated and edited by Howard, M., and Paret, P. Princeton University Press, Princeton, NJ.

Cohen, L. J. (1977). The Probable and the Provable, Clarendon Press, Oxford. UK.

Cohen, L. J. (1989). An Introduction to the Philosophy of Induction and Probability, Clarendon Press, Oxford, UK.

Cohen, M. R., and Nagel, E. (1934). An Introduction to Logic and Scientific Method, Harcourt, Brace, New York, NY, pp. 274–275.

Cohen, P., Schrag, R., Jones, E., Pease, A., Lin, A., Starr, B., Gunning, D., and Burke, M. (1998). The DARPA High-Performance Knowledge Bases Project, AI Magazine, vol. 19, no. 4, pp. 25–49.Google Scholar

Cooper, T., and Wogrin, N. (1988). Rule-based Programming with OPS5, Morgan Kaufmann, San Mateo, CA.

Cross, S. E., and Walker, E. (1994). DART: Applying Knowledge-based Planning and Scheduling to Crisis Action Planning, in Zweben, M., and Fox, M. S. (eds.), Intelligent Scheduling, Morgan Kaufmann, San Mateo, CA, pp. 711–729.

Cyc (2008). OpenCyc Just Got Better – Much Better! www.opencyc.org (accessed August 22, 2008).

Cyc (2016). The Cyc homepage, www.cyc.com (accessed February 3, 2016).

Dale, A. I. (2003). Most Honourable Remembrance: The Life and Work of Thomas Bayes, Springer-Verlag, New York, NY.

David, F. N. (1962). Games, Gods, and Gambling, Griffin, London, UK.

David, P. A., and Foray, D. (2003). Economic Fundamentals of the Knowledge Society, Policy Futures in Education. An e-Journal, vol. 1, no. 1, Special Issue: Education and the Knowledge Economy, January, pp. 20–49.Google Scholar

Davies, T. R., and Russell, S. J. (1990). A Logical Approach to Reasoning by Analogy, in Shavlik, J., and Dietterich, T. (eds.), Readings in Machine Learning, Morgan Kaufmann, San Mateo, CA, pp. 657–663.

DeJong, G., and Mooney, R. (1986). Explanation-based Learning: An Alternative View, Machine Learning, vol. 1, pp. 145–176.Google Scholar

Department of Homeland Security (DHS) (2004). National Response Plan.

Desai, M. (2009). Persistent Stare Exploitation and Analysis System (PerSEAS), DARPA-BAA-09-55, https://www.fbo.gov/index?s=opportunity&mode=form&id=eb5dd436ac371ce79d91c84ec4e91341&tab=core&_cview=1 (accessed April 13, 2016)

DOLCE (2012). Laboratory for Applied Ontology, www.loa-cnr.it/DOLCE.html (accessed August 31, 2012)

Drucker, P. (1993). Post-Capitalist Society, HarperCollins, New York.

Durham, S. (2000). Product-Centered Approach to Information Fusion, AFOSR Forum on Information Fusion, Arlington, VA, October 18–20.

Durkin, J. (1994). Expert Systems: Design and Development, Prentice Hall, Englewood Cliffs, NJ.

Dybala, T. (1996). Shared Expertise Model for Building Interactive Learning Agents, PhD Dissertation, School of Information Technology and Engineering, George Mason University, Fairfax, VA. lac.gmu.edu/publications/data/1996/Dybala-PhD-abs.pdf (accessed April 12, 2016)

Echevarria, A. J. (2003). Reining in the Center of Gravity Concept. Air & Space Power Journal, vol. XVII, no. 2, pp. 87–96.Google Scholar

Eco, U., and Sebeok, T. (eds.) (1983). The Sign of Three: Dupin, Holmes, Peirce, Indiana University Press: Bloomington.

Eikmeier, D. C. (2006). Linking Ends, Ways and Means with Center of Gravity Analysis. Carlisle Barracks, U.S. Army War College, Carlisle, PA.

Einstein, A. (1939). Letter from Albert Einstein to President Franklin D. Roosevelt: 08/02/1939. The letter itself is in the Franklin D. Roosevelt Library in Hyde Park, NY. See the National Archives copy in pdf form at media.nara.gov/Public_Vaults/00762_.pdf (accessed November 16, 2014).

EXPECT (2015). The EXPECT homepage, www.isi.edu/ikcap/expect/ (accessed May 25, 2015).

Farquhar, A., Fikes, R., and Rice, J. (1997). The Ontolingua Server: A Tool for Collaborative Ontology Construction, International Journal of Human–Computer Studies, vol. 46, no. 6, pp. 707–727.Google Scholar

Federal Rules of Evidence (2009). 2009–2010 ed. West Publishing, St. Paul, MN.

Feigenbaum, E. A. (1982). Knowledge Engineering for the 1980s, Research Report, Stanford University, Stanford, CA.

Feigenbaum, E. A. (1993). Tiger in a Cage: The Applications of Knowledge-based Systems, Invited Talk, AAAI-93 Proceedings, www.aaai.org/Papers/AAAI/1993/AAAI93-127.pdf. (accessed April 13, 2016)

Fellbaum, C. (ed.) (1988). WordNet: An Electronic Lexical Database. MIT Press, Cambridge, MA.

FEMA (Federal Emergency Management Agency) (2007). National Incident Management System. www.fema.gov/national-incident-management-system (accessed April 13, 2016).

Ferrucci, D., Brown, E., Chu-Caroll, J., Fan, J., Gondek, D., Kalynapur, A. A., Murdoch, J. W., Nyberg, E., Prager, J., Schlaefer, N., and Welty, C. (2010). Building Watson: An Overview of the DeepQA Project, AI Magazine, vol. 31, no. 3, pp. 59–79.Google Scholar

Filip, F. G. (1989). Creativity and Decision Support System, Studies and Researches in Computer and Informatics, vol. 1, no. 1, pp. 41–49.Google Scholar

Filip, F. G. (ed.) (2001). Informational Society–Knowledge Society, Expert, Bucharest.

FM 100–5. (1993). U.S. Army Field Manual 100–5, Operations, Headquarters, Department of the Army, Washington, DC.

FOAF (2012). The Friend of a Friend (FOAF) project. www.foaf-project.org/ (accessed August 31, 2012)

Forbes (2013). www.forbes.com/profile/michael-chipman/ (accessed September 2, 2013)

Forbus, K. D., Gentner, D., and Law, K. (1994). MAC/FAC: A Model of Similarity-Based Retrieval, Cognitive Science, vol. 19, pp. 141–205.Google Scholar

Friedman-Hill, E. (2003). Jess in Action, Manning, Shelter Island, NY.

Gammack, J. G. (1987). Different Techniques and Different Aspects on Declarative Knowledge, in Kidd, A. L. (ed.), Knowledge Acquisition for Expert Systems: A Practical Handbook, Plenum Press, New York, NY, and London, UK.

Gentner, D. (1983). Structure Mapping: A Theoretical Framework for Analogy, Cognitive Science, vol. 7, pp. 155–170.Google Scholar

Geonames (2012). GeoNames Ontology–Geo Semantic Web. [Online] www.geonames.org/ontology/documentation.html (accessed August 31, 2012)

GFO (2012). General Formal Ontology (GFO). [Online] www.onto-med.de/ontologies/gfo/ (accessed August 31, 2012)

Ghallab, M., Nau, D., and Traverso, P. (2004). Automatic Planning: Theory and Practice, Morgan Kaufmann, San Mateo, CA.

Giarratano, J., and Riley, G. (1994). Expert Systems: Principles and Programming, PWS, Boston, MA.

Gil, Y., and Paris, C. (1995). Towards model-independent knowledge acquisition, in Tecuci, G., and Kodratoff, Y. (eds.), Machine Learning and Knowledge Acquisition: Integrated Approaches, Academic Press, Boston, MA.

Giles, P. K., and Galvin, T. P. (1996). Center of Gravity: Determination, Analysis and Application. Carlisle Barracks, U.S. Army War College, Carlisle, PA.

Goodman, D., and Keene, R. (1997). Man versus Machine: Kasparov versus Deep Blue, H3 Publications, Cambridge, MA.

Gruber, T. R. (1993). A Translation Approach to Portable Ontology Specification. Knowledge Acquisition, vol. 5, pp. 199–220.Google Scholar

Guizzardi, G., and Wagner, G. (2005a). Some Applications of a Unified Foundational Ontology in Business, in Rosemann, M., and Green, P. (eds.), Ontologies and Business Systems Analysis, IDEA Group, Hershey, PA.

Guizzardi, G., and Wagner, G. (2005b). Towards Ontological Foundations for Agent Modeling Concepts Using UFO, in Agent-Oriented Information Systems (AOIS), selected revised papers of the Sixth International Bi-Conference Workshop on Agent-Oriented Information Systems. Springer-Verlag, Berlin and Heidelberg, Germany.

Hieb, M. R. (1996). Training Instructable Agents through Plausible Version Space Learning, PhD Dissertation, School of Information Technology and Engineering, George Mason University, Fairfax, VA.

Hobbs, J. R., and Pan, F. (2004). An Ontology of Time for the Semantic Web, CM Transactions on Asian Language Processing (TALIP), vol. 3, no. 1 (special issue on temporal information processing), pp. 66–85.Google Scholar

Horvitz, E. (1999). Principles of Mixed-Initiative User Interfaces, in Proceedings of CHI '99, ACM SIGCHI Conference on Human Factors in Computing Systems, Pittsburgh, PA, May. ACM Press, New York, NY. research.microsoft.com/~horvitz/uiact.htm (accessed April 13, 2016)

Humphreys, B. L., and Lindberg, D.A.B. (1993). The UMLS Project: Making the Conceptual Connection between Users and the Information They Need, Bulletin of the Medical Library Association, vol. 81, no. 2, p. 170.Google Scholar

Jackson, P. (1999). Introduction to Expert Systems, Addison-Wesley, Essex, UK.

Jena (2012). Jena tutorial. jena.sourceforge.net/tutorial/index.html (accessed August 4, 2012).

JESS (2016). The rule engine for the JAVA platform, JESS webpage: www.jessrules.com/jess/download.shtml (accessed February 3, 2016)

Joint Chiefs of Staff (2008). Joint Operations, Joint Pub 3-0, U.S. Joint Chiefs of Staff, Washington, DC.

Jones, E. (1998). HPKB Year 1 End-to-End Battlespace Challenge Problem Specification, Alphatech, Burlington, MA.

Kant, I. (1781). The Critique of Pure Reason, Project Gutenberg, www.gutenberg.org/ebooks/4280 (accessed, August 19, 2013)

Keeling, H. (1998). A Methodology for Building Verified and Validated Intelligent Educational Agents – through an Integration of Machine Learning and Knowledge Acquisition, PhD Dissertation, School of Information Technology and Engineering, George Mason University, Fairfax, VA.

Kent, S. (1994). Words of Estimated Probability, in Steury, D. P. (ed.), Sherman Kent and the Board of National Estimates: Collected Essays, Center for the Study of Intelligence, CIA, Washington, DC.

Kim, J., and Gil, Y. (1999). Deriving Expectations to Guide Knowledge Base Creation, in Proceedings of AAAI-99/IAAI-99, AAAI Press, Menlo Park, CA, pp. 235–241.

Kneale, W. (1949). Probability and Induction, Clarendon Press, Oxford, UK. pp. 30–37.

Kodratoff, Y., and Ganascia, J-G. (1986). Improving the Generalization Step in Learning, in Michalski, R., Carbonell, J., and Mitchell, T. (eds.), Machine Learning: An Artificial Intelligence Approach, vol. 2. Morgan Kaufmann, San Mateo, CA, pp. 215–244.

Kolmogorov, A. N. (1933 [1956]). Foundations of a Theory of Probability, Chelsea, New York, NY, pp. 3–4.

Kolmogorov, A. N. (1969). The Theory of Probability, in Aleksandrov, A. D., Kolmogorov, A. N., and Lavrentiev, M. A. (eds.), Mathematics: Its Content, Methods, and Meaning, vol. 2, MIT Press, Cambridge, MA, pp. 231–264.

Langley, P. W. (2012). The Cognitive Systems Paradigm, Advances in Cognitive Systems, vol. 1, pp. 3–13.Google Scholar

Laplace, P. S. (1814). Théorie Analytique des Probabilités, Paris, Ve. Courcier, archive.org/details/thorieanalytiqu01laplgoog (accessed January 28, 2016)

Le, V. (2008). Abstraction of Reasoning for Problem Solving and Tutoring Assistants. PhD Dissertation in Information Technology. Learning Agents Center, Volgenau School of IT&E, George Mason University, Fairfax, VA.

Lempert, R. O., Gross, S. R., and Liebman, J. S. (2000). A Modern Approach to Evidence, West Publishing, St. Paul, MN, pp. 1146–1148.

Lenat, D. B. (1995). Cyc: A Large-scale Investment in Knowledge Infrastructure, Communications of the ACM, vol. 38, no. 11, pp. 33–38.Google Scholar

Loom (1999). Retrospective on LOOM. www.isi.edu/isd/LOOM/papers/macgregor/Loom_Retrospective.html (accessed August 4, 2012)

MacGregor, R. (1991). The Evolving Technology of Classification-Based Knowledge Representation Systems, in Sowa, J. (ed.), Principles of Semantic Networks: Explorations in the Representations of Knowledge, Morgan Kaufmann, San Francisco, CA, pp. 385–400.

Marcu, D. (2009). Learning of Mixed-Initiative Human-Computer Interaction Models, PhD Dissertation in Computer Science. Learning Agents Center, Volgenau School of IT&E, George Mason University, Fairfax, VA.

Marcus, S. (1988). SALT: A Knowledge-Acquisition Tool for Propose-and-Revise Systems, in Marcus, S. (ed.), Automating Knowledge Acquisition for Expert Systems, Kluwer Academic., Norwell, MA, pp. 81–123.

Masolo, C., Vieu, L., Bottazzi, E., Catenacci, C., Ferrario, R., Gangemi, A., and Guarino, N. (2004). Social Roles and Their Descriptions, in Dubois, D., Welty, C., and Williams, M-A. (eds.), Principles of Knowledge Representation and Reasoning: Proceedings of the Ninth International Conference (KR2004), AAAI Press, Menlo Park, CA, pp. 267–277.

McDermott, J. (1982). R1: A Rule-Based Configurer of Computer Systems, Artificial Intelligence Journal, vol. 19, no. 1, pp. 39–88.Google Scholar

Meckl, S., Tecuci, G., Boicu, M., and Marcu, D. (2015). Towards an Operational Semantic Theory of Cyber Defense against Advanced Persistent Threats, in Laskey, K. B., Emmons, I., Costa, P. C. G., and Oltramari, A. (eds.), Proceedings of the Tenth International Conference on Semantic Technologies for Intelligence, Defense, and Security – STIDS 2015, pp. 58–65, Fairfax, VA, November 18–20. lac.gmu.edu/publications/2015/APT-LAC.pdf (accessed January 12, 2016)

Michalski, R. S. (1986). Understanding the Nature of Learning: Issues and Research Directions, in Michalski, R. S., Carbonell, J. G., and Mitchell, T. (eds.), Machine Learning, vol. 2, Morgan Kaufmann, Los Altos, CA, pp. 3–25.

Michalski, R. S., and Tecuci, G. (eds.) (1994). Machine Learning: A Multistrategy Approach, vol. IV, Morgan Kaufmann, San Mateo, CA. store.elsevier.com/Machine-Learning/isbn-9781558602519/ (accessed May 29, 2015)

Minsky, M. (1986). The Society of Mind, Simon and Schuster, New York, NY.

Mitchell, T. M. (1978). Version Spaces: An Approach to Concept Learning. PhD Dissertation, Stanford University, Stanford, CA.

Mitchell, T. M. (1997). Machine Learning. McGraw-Hill, New York, NY.

Mitchell, T. M., Keller, R. M., and Kedar-Cabelli, S. T. (1986). Explanation-Based Generalization: A Unifying View, Machine Learning, vol. 1, pp. 47–80.Google Scholar

Murphy, P. (2003). Evidence, Proof, and Facts: A Book of Sources. Oxford University Press, Oxford, UK.

Musen, M. A. (1989). Automated Generation of Model-based Knowledge Acquisition Tools, Morgan Kaufmann., San Francisco, CA.

NAE (National Academy of Engineering) (2008). Grand Challenges for Engineering. www.engineeringchallenges.org/cms/challenges.aspx (accessed April 13, 2016)

Nau, D., Au, T., Ilghami, O., Kuter, U., Murdock, J., Wu, D., and Yaman, F. (2003). SHOP2: An HTN Planning System, Journal of Artificial Intelligence Research, vol. 20, pp. 379–404.Google Scholar

Negoita, C. V., and Ralescu, D. A. (1975). Applications of Fuzzy Sets to Systems Analysis, Wiley, New York, NY.

Nilsson, N. J. (1971). Problem Solving Methods in Artificial Intelligence, McGraw-Hill, New York, NY.

Nonaka, I., and Krogh, G. (2009). Tacit Knowledge and Knowledge Conversion: Controversy and Advancement in Organizational Knowledge Creation Theory, Organization Science, vol. 20, no. 3 (May–June), pp. 635–652. www.ai.wu.ac.at/~kaiser/birgit/Nonaka-Papers/tacit-knowledge-and-knowledge-conversion-2009.pdf (accessed April 13, 2016)Google Scholar

Noy, N. F., and McGuinness, D. L. (2001). Ontology Development 101: A Guide to Creating Your First Ontology. Stanford Knowledge Systems Laboratory Technical Report KSL-01–05 and Stanford Medical Informatics Technical Report SMI-2001-0880, March, Stanford, CA.

NRC (National Research Council) (1996). National Research Council: National Science Education Standards. National Academy Press, Washington, DC. www.nap.edu/openbook.php?record_id=4962 (accessed April 13, 2016)

NRC (National Research Council) (2000). National Research Council: Inquiry and the National Science Education Standards, National Academy Press, Washington, DC. www.nap.edu/catalog.php?record_id=9596 (accessed April 13, 2016)

NRC (National Research Council) (2010). Preparing Teachers: Building Evidence for Sound Policy, National Academies Press, Washington, DC. www.nap.edu/catalog.php?record_id=12882 (accessed April 13, 2016)

NRC (National Research Council) (2011). A Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas. www.nap.edu/catalog.php?record_id=13165 (accessed April 13, 2016)

Obrst, L., Chase, P., and Markeloff, R. (2012). Developing an Ontology of the Cyber Security Domain, in Proceedings of the Seventh International Conference on Semantic Technologies for Intelligence, Defense, and Security – STIDS, October 23–26, Fairfax, VA.

OKBC (Open Knowledge Based Connectivity) (2008). OKBC homepage. www.ksl.stanford.edu/software/OKBC/ (accessed August 4, 2012)

O'Keefe, R. M., Balci, O., and Smith, E. P. (1987). Validating Expert Systems Performance, IEEE Expert, no. 2, vol. 4, pp. 81–90.Google Scholar

Oldroyd, D. (1986). The Arch of Knowledge: An Introductory Study of the History of the Philosophy and Methodology of Science, Routledge Kegan & Paul, London, UK.

Ontolingua (1997). Ontolingua System Reference Manual. www-ksl-svc.stanford.edu:5915/doc/frame-editor/index.html (accessed August 4, 2012).

Ontolingua (2008). The Ontolingua homepage. www.ksl.stanford.edu/software/ontolingua/ (accessed August 4, 2012).

OWLIM (2012). OWLIM family of semantic repositories, or RDF database management systems. www.ontotext.com/owlim (accessed August 4, 2012).

Pan, F., and Hobbs, J. R. (2004). Time in OWL-S. Proceedings of the AAAI Spring Symposium on Semantic Web Services, Stanford University, Stanford, CA, AAAI Press, Palo Alto, CA, pp. 29–36.

Pan, F., and Hobbs, J. R. (2012). A Time Zone Resource in OWL. www.isi.edu/~hobbs/timezonehomepage.html (accessed August 31, 2012)

Pease, A. (2011). Ontology: A Practical Guide, Articulate Software Press, Angwin, CA. www.ontologyportal.org/Book.html (accessed April 13, 2016)

Peirce, C. S. (1898 [1992]). Reasoning and the Logic of Things, edited by Ketner, K., Harvard University Press, Cambridge, MA.

Peirce, C. S. (1901 [1955]). Abduction and Induction, in Philosophical Writings of Peirce, edited by Buchler, J., Dover, New York, NY, pp. 150–156.

Pellet (2012). OWL 2 Reasoner for Java. clarkparsia.com/pellet/ (accessed August 4, 2012).

Plotkin, G. D. (1970). A Note on Inductive Generalization, in Meltzer, B., and Michie, D. (eds.), Machine Intelligence 5, Edinburgh University Press, Edinburgh, UK, pp. 165–179.

Pomerol, J. C., and Adam, F. (2006). On the Legacy of Herbert Simon and His Contribution to Decision-making Support Systems and Artificial Intelligence, in Gupta, J. N. D., Forgionne, G. A., and Mora, M. T. (eds.), Intelligent Decision-Making Support Systems: Foundations, Applications and Challenges, Springer-Verlag, London, UK, pp. 25–43.

Powell, G. M., and Schmidt, C. F. (1988). A First-order Computational Model of Human Operational Planning, CECOM-TR-01–8, U.S. Army CECOM, Fort Monmouth, NJ.

Protégé (2000). The Protégé Project. protege.stanford.edu (accessed April 13, 2016)

Protégé (2015). Protégé ontology editor and knowledge base framework, homepage. protege.stanford.edu (accessed May 25, 2015).

Puppe, F. (1993). Problem Classes and Problem Solving Methods, in Systematic Introduction to Expert Systems: Knowledge Representations and Problem Solving Methods, Springer Verlag, Berlin and Heidelberg, Germany, pp. 87–112.

Ressler, J., Dean, M., and Kolas, D. (2010). Geospatial Ontology Trade Study, in Janssen, T., Ceuster, W., and Obrst, L. (eds.), Ontologies and Semantic Technologies for Intelligence, IOS Press, Amsterdam, Berlin, Tokyo, and Washington, DC, pp. 179–212.

Rooney, D., Hearn, G., and Ninan, A. (2005). Handbook on the Knowledge Economy, Edward Elgar, Cheltenham, UK.

Russell, S. J., and Norvig, P. (2010). Artificial Intelligence: A Modern Approach, Prentice Hall, Upper Saddle River, NJ, pp. 34–63.

Schneider, L. (2003). How to Build a Foundational Ontology – the Object-centered High-level Reference Ontology OCHRE, in Proceedings of the 26th Annual German Conference on AI, KI 2003: Advances in Artificial Intelligence, Springer-Verlag, Heidelberg, Germany, pp. 120–134.

Schreiber, G., Akkermans, H., Anjewierden, A., de Hoog, R., Shadbolt, N., Van de Velde, W., and Wielinga, B. (2000). Knowledge Engineering and Management: The Common KADS Methodology, MIT Press, Cambridge, MA.

Schum, D. A. (1987). Evidence and Inference for the Intelligence Analyst (2 vols), University Press of America, Lanham, MD.

Schum, D. A. (1989). Knowledge, Probability, and Credibility, Journal of Behavioral Decision Making, vol. 2, pp. 39–62.Google Scholar

Schum, D. A. (1991). Jonathan Cohen and Thomas Bayes on the Analysis of Chains of Reasoning, in Eells, E., and Maruszewski, T. (eds.), Probability and Rationality: Studies on L. Jonathan Cohen's Philosophy of Science, Editions Rodopi, Amsterdam, Netherlands, pp. 99–145.

Schum, D. A. (1999). Marshaling Thoughts and Evidence during Fact Investigation, South Texas Law Review, vol. 40, no. 2 (summer), pp. 401–454.Google Scholar

Schum, D. A. (1994 [2001a]). The Evidential Foundations of Probabilistic Reasoning, Northwestern University Press, Evanston, IL.

Schum, D. A. (2001b). Species of Abductive Reasoning in Fact Investigation in Law, Cardozo Law Review, vol. 22, nos. 5–6, pp. 1645–1681.Google Scholar

Schum, D. A. (2011). Classifying Forms and Combinations of Evidence: Necessary in a Science of Evidence, in Dawid, P., Twining, W., and Vasilaki, M. (eds.), Evidence, Inference and Inquiry, British Academy, Oxford University Press, Oxford, UK, pp. 11–36.

Schum, D. A., and Morris, J. (2007). Assessing the Competence and Credibility of Human Sources of Evidence: Contributions from Law and Probability, Law, Probability and Risk, vol. 6, pp. 247–274.Google Scholar

Schum, D. A., Tecuci, G., and Boicu, M. (2009). Analyzing Evidence and Its Chain of Custody: A Mixed-Initiative Computational Approach, International Journal of Intelligence and Counterintelligence, vol. 22, pp. 298–319. lac.gmu.edu/publications/2009/Schum%20et%20al%20-%20Chain%20of%20Custody.pdf (accessed April 13, 2016)Google Scholar

Shafer, G. (1976). A Mathematical Theory of Evidence, Princeton University Press, Princeton, NJ.

Shafer, G. (1988). Combining AI and OR, University of Kansas School of Business Working Paper No. 195, April.

Simon, H. (1983). Why Should Machines Learn? in Michalski, R. S., Carbonell, J. G., and Mitchell, T.M. (eds.), Machine Learning, vol. 1, Morgan Kaufmann, Los Altos, CA, pp. 25–38.

Simonite, T. (2013). Bill Gates: Software Assistants Could Help Solve Global Problems, MIT Technology Review, July 16. www.technologyreview.com/news/517171/bill-gates-software-assistants-could-help-solve-global-problems/ (accessed April 13, 2016)

Siri (2011). Apple's Siri homepage. www.apple.com/ios/siri/ (accessed April 12, 2016)

Strange, J. (1996). Centers of Gravity & Critical Vulnerabilities: Building on the Clausewitzian Foundation so That We Can All Speak the Same Language, Marine Corps University Foundation, Quantico, VA.

Strange, J., and Iron, R. (2004a). Understanding Centers of Gravity and Critical Vulnerabilities, Part 1: What Clausewitz (Really) Meant by Center of Gravity. www.au.af.mil/au/awc/awcgate/usmc/cog1.pdf (accessed May 25, 2015)

Strange, J., and Iron, R. (2004b). Understanding Centers of Gravity and Critical Vulnerabilities, Part 2: The CG-CC-CR-CV Construct: A Useful Tool to Understand and Analyze the Relationship between Centers of Gravity and Their Critical Vulnerabilities. www.au.af.mil/au/awc/awcgate/usmc/cog2.pdf (accessed May 25, 2015)

Tate, A. (1977). Generating Project Networks, in Proceedings of IJCAI-77, Boston, MA, Morgan Kaufmann, San Francisco, CA, pp. 888–893.

Tecuci, G. (1988). Disciple: A Theory, Methodology and System for Learning Expert Knowledge , Thèse de Docteur en Science , University of Paris-South. lac.gmu.edu/publications/1988/TecuciG_PhD_Thesis.pdf (accessed April 13, 2016)

Tecuci, G. (1992). Automating Knowledge Acquisition as Extending, Updating, and Improving a Knowledge Base, IEEE Transactions on Systems, Man and Cybernetics, vol. 22, pp. 1444–1460. lac.gmu.edu/publications/1992/TecuciG_Automating_Knowledge_Acquisition.pdf (accessed April 13, 2016)Google Scholar

Tecuci, G. (1993). Plausible Justification Trees: A Framework for the Deep and Dynamic Integration of Learning Strategies, Machine Learning Journal, vol. 11, pp. 237–261. lac.gmu.edu/publications/1993/TecuciG_Plausible_Justification_Trees.pdf (accessed April 13, 2016)Google Scholar

Tecuci, G. (1998). Building Intelligent Agents: An Apprenticeship Multistrategy Learning Theory, Methodology, Tool and Case Studies, Academic Press, London, UK. lac.gmu.edu/publications/1998/TecuciG_Building_Intelligent_Agents/default.htm (accessed April 13, 2016)

Tecuci, G., and Keeling, H. (1999). Developing an Intelligent Educational Agent with Disciple, International Journal of Artificial Intelligence in Education, vol. 10, no. 3–4. lac.gmu.edu/publications/1999/TecuciG_Intelliget_Educational_Agent.pdf (accessed April 13, 2016)Google Scholar

Tecuci, G., and Kodratoff, Y. (1990). Apprenticeship Learning in Imperfect Theory Domains, in Kodratoff, Y., and Michalski, R. S. (eds.), Machine Learning: An Artificial Intelligence Approach, vol. 3, Morgan Kaufmann, San Mateo, CA, pp. 514–551. lac.gmu.edu/publications/data/1990/apprenticeship_1990.pdf (accessed April 13, 2016)

Tecuci, G., and Kodratoff, Y. (eds.) (1995). Machine Learning and Knowledge Acquisition: Integrated Approaches, Academic Press, London, UK. lac.gmu.edu/publications/1995/TecuciG_MLKA_Integrated_Approaches.pdf (accessed April 13, 2016)

Tecuci, G., and Michalski, R. S. (1991). A Method for Multistrategy Task-Adaptive Learning Based on Plausible Justifications, in Birnbaum, L., and Collins, G. (eds.), Machine Learning: Proceedings of the Eighth International Conference, June, Chicago, IL, Morgan Kaufmann, San Mateo, CA, pp. 549–553. lac.gmu.edu/publications/1991/TecuciG_Multistrategy_Learning_Method.pdf (accessed April 13, 2016)

Tecuci, G., Kedar, S., and Kodratoff, Y. (guest eds.) (1994). Knowledge Acquisition Journal, vol. 6, no. 2 (special issue on the integration of machine learning and knowledge acquisition), pp. 89–214.

Tecuci, G., Boicu, M., Wright, K., Lee, S. W., Marcu, D., and Bowman, M. (1999). An Integrated Shell and Methodology for Rapid Development of Knowledge-based Agents, in Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI-99), July 18–22, Orlando, FL, AAAI Press, Menlo Park, CA, pp. 250–257. lac.gmu.edu/publications/data/1999/ismrdkba.pdf (accessed April 13, 2016)

Tecuci, G., Boicu, M., Wright, K., Lee, S. W., Marcu, D. and Bowman, M. (2000). A Tutoring Based Approach to the Development of Intelligent Agents, in Teodorescu, H. N., Mlynek, D., Kandel, A., and Zimmermann, H. J. (eds.), Intelligent Systems and Interfaces, Kluwer Academic Press, Boston, MA. lac.gmu.edu/publications/data/2000/2000_Disciple-Planning.pdf (accessed April 13, 2016)

Tecuci, G., Boicu, M., Bowman, M., and Marcu, D., with commentary by Burke, M. (2001). An Innovative Application from the DARPA Knowledge Bases Programs: Rapid Development of a Course of Action Critiquer, AI Magazine, vol. 22, no. 2, pp. 43–61. lac.gmu.edu/publications/2001/TecuciG_Disciple_COA_IAAI.pdf (accessed April 13, 2016)Google Scholar

Tecuci, G., Boicu, M., Marcu, D., Stanescu, B., Boicu, C., Comello, J., Lopez, A., Donlon, J., and Cleckner, W. (2002a). Development and Deployment of a Disciple Agent for Center of Gravity Analysis, in Proceedings of the Eighteenth National Conference of Artificial Intelligence and the Fourteenth Conference on Innovative Applications of Artificial Intelligence, AAAI-02/IAAI-02, Edmonton, Alberta, Canada, AAAI Press/MIT Press, New York, NY, and Cambridge, MA, pp. 853–860. lac.gmu.edu/publications/data/2002/dddacga.pdf (accessed April 13, 2016)

Tecuci, G., Boicu, M., Marcu, D., Stanescu, B., Boicu, C., and Comello, J. (2002b). Training and Using Disciple Agents: A Case Study in the Military Center of Gravity Analysis Domain, AI Magazine, vol. 24, no. 4, pp. 51–68. lac.gmu.edu/publications/2002/TecuciG_Disciple_COG_IAAI.pdf (accessed April 13, 2016)Google Scholar

Tecuci, G., Boicu, M., Ayers, C., and Cammons, D. (2005a). Personal Cognitive Assistants for Military Intelligence Analysis: Mixed-Initiative Learning, Tutoring, and Problem Solving, in Proceedings of the First International Conference on Intelligence Analysis, May 2–6, McLean, VA, MITRE Corporation, Bedford, MA. lac.gmu.edu/publications/data/2005/Tecuci-Disciple-LTA.pdf (accessed April 13, 2016)

Tecuci, G., Boicu, M., Boicu, C., Marcu, D., Stanescu, B., and Barbulescu, M. (2005b). The Disciple-RKF Learning and Reasoning Agent, Computational Intelligence, vol. 21, no. 4, pp. 462–479. lac.gmu.edu/publications/2005/TecuciG_Disciple_RKF_CI.pdf (accessed April 13, 2016)Google Scholar

Tecuci, G., Boicu, M., and Cox, M. T. (guest eds.) (2007a). AI Magazine, vol. 28, no. 2 (special issue on mixed-initiative assistants). www.aaai.org/ojs/index.php/aimagazine/issue/view/174/showToc (accessed May 29, 2015)

Tecuci, G., Boicu, M., and Cox, M. T. (2007b). Seven Aspects of Mixed-Initiative Reasoning: An Introduction to the Special Issue on Mixed-Initiative Assistants, AI Magazine, vol. 28, no. 2 (special issue on mixed-initiative assistants), pp. 11–18. lac.gmu.edu/publications/2007/BoicuM_AIMagazine_Intro.pdf (accessed April 13, 2016)Google Scholar

Tecuci, G., Boicu, M., Marcu, D., Boicu, C., Barbulescu, M., Ayers, C., and Cammons, D. (2007c). Cognitive Assistants for Analysts, Journal of Intelligence Community Research and Development (JICRD). Also published in Auger, J., and Wimbish, W. (eds.) (2007). Proteus Futures Digest: A Compilation of Selected Works Derived from the 2006 Proteus Workshop, Proteus Management Group, Carlisle Barracks, PA, pp. 303–329. lac.gmu.edu/publications/2007/TecuciG_Cognitive_Assistants.pdf (accessed April 13, 2016)

Tecuci, G., Boicu, M., Hajduk, T., Marcu, D., Barbulescu, M., Boicu, C., and Le, V. (2007d). A Tool for Training and Assistance in Emergency Response Planning, in Proceedings of the Hawaii International Conference on System Sciences, HICSS40, January 3–6, Hawaii, IEEE Computer Society Press. lac.gmu.edu/publications/2007/Disciple-VPT%20Hawaii.pdf (accessed April 13, 2016)

Tecuci, G., Boicu, M., Marcu, D., Boicu, C., and Barbulescu, M. (2008a). Disciple-LTA: Learning, Tutoring and Analytic Assistance, Journal of Intelligence Community Research and Development (JICRD), July. lac.gmu.edu/publications/2008/Disciple-LTA08.pdf (accessed April 13, 2016)

Tecuci, G., Boicu, M., and Comello, J. (2008b). Agent-Assisted Center of Gravity Analysis, CD with Disciple-COG and Lecture Notes used in courses at the U.S. Army War College and Air War College. George Mason University Press, Fairfax, VA. lac.gmu.edu/cog-book/ (accessed April 13, 2016)

Tecuci, G., Boicu, M., Marcu, D., Barbulescu, M., Boicu, C., Le, V., and Hajduk, T. (2008c). Teaching Virtual Experts for Multi-Domain Collaborative Planning, Journal of Software, vol. 3, no. 3 (March), pp. 38–59. lac.gmu.edu/publications/2008/TecuciG_Disciple_VE_JS.pdf (accessed April 13, 2016)Google Scholar

Tecuci, G., Schum, D. A., Boicu, M., Marcu, D., and Hamilton, B. (2010a). Intelligence Analysis as Agent-Assisted Discovery of Evidence, Hypotheses and Arguments, in Phillips-Wren, G., Jain, L.C., Nakamatsu, K., and Howlett, R.J., (eds.), Advances in Intelligent Decision Technologies, SIST 4, Springer-Verlag, Berlin and Heidelberg Germany, pp. 1–10. lac.gmu.edu/publications/2010/Tecuci-Discovery-in-motion-imagery.pdf (accessed April 13, 2016)

Tecuci, G., Schum, D. A., Boicu, M., Marcu, D., Hamilton, B., and Wible, B. (2010b). Teaching Intelligence Analysis with TIACRITIS, American Intelligence Journal, vol. 28, no. 2 (December), pp. 50–65. lac.gmu.edu/publications/2010/Tiacritis-AIJ.pdf (accessed April 13, 2016)Google Scholar

Tecuci, G., Marcu, D., Boicu, M., Schum, D. A., and Russell, K. (2011a). Computational Theory and Cognitive Assistant for Intelligence Analysis, in Proceedings of the Sixth International Conference on Semantic Technologies for Intelligence, Defense, and Security – STIDS, November 16–18, Fairfax, VA, pp. 68–75. ceur-ws.org/Vol-808/STIDS2011_CR_T9_TecuciEtAl.pdf (accessed May 29, 2015)

Tecuci, G., Schum, D. A., Boicu, M., and Marcu, D. (2011b). Introduction to Intelligence Analysis: A Hands-on Approach with TIACRITIS, Learning Agents Center, George Mason University, Fairfax, VA (1st ed., 2010).

Tecuci, G., Schum, D. A., Marcu, D., and Boicu, M. (2014). Computational Approach and Cognitive Assistant for Evidence-based Reasoning in Intelligence Analysis, International Journal of Intelligent Defence Support Systems, vol. 5, no. 2, pp. 146–172. lac.gmu.edu/publications/2014/Disciple-CD-IJIDSS.pdf (accessed April 13, 2016)Google Scholar

Tecuci, G., Marcu, D., Boicu, M., and Schum, D. A. (2015). COGENT: Cognitive Agent for Cogent Analysis, Proceedings of the 2015 AAAI Fall Symposium 'Cognitive Assistance in Government and Public Sector Applications, pp. 58–65, Arlington, VA, November 12–14. lac.gmu.edu/publications/2015/Cogent-overview.pdf (accessed January 31, 2016)

Tecuci, G., Schum, D. A., Marcu, D., and Boicu, M. (2016). Intelligence Analysis as Discovery of Evidence, Hypotheses, and Arguments: Connecting the Dots, Cambridge University Press, New York, NY.

Toffler, A. (1984). Science and Change, foreword to Prigogine, Ilya, and Stengers, Isabelle, Order out of Chaos: Man's New Dialogue with Nature, Bantam, New York, NY, pp. xi–xxvi.

TopBraid Composer (2012). TopBraid Composer Ontology Development Tool. www.topquadrant.com/products/TB_Composer.html (accessed August 4, 2012).

Toulmin, S. E. (1963). The Uses of Argument, Cambridge University Press, Cambridge, UK.

Turing, A. (1950). Computing Machinery and Intelligence, Mind, vol. 59, pp. 433–460.Google Scholar

Turoff, M. (2007). Design of Interactive Systems, in Emergency Management Information Systems Tutorial, 40th Hawaii International Conference on System Sciences, HICSS-40, Hawaii, January 3.

UMLS (2012). Unified Medical Language System, U.S. National Library of Medicine. www.nlm.nih.gov/research/umls/ (accessed August 4, 2012)

UNESCO (2005). Toward Knowledge Societies, unesdoc.unesco.org/images/0014/001418/141843e.pdf (accessed October 1, 2011)

Van Gelder, T. J. (2007). The Rationale for Rationale, Law, Probability and Risk, vol. 6, pp. 23–42.Google Scholar

Van Melle, W., Scott, A. C., Bennett, J. S., and Peairs, M. (1981). The EMYCIN Manual, Report No. HPP-81-16, Computer Science Department, Stanford University, Stanford, CA.

Veloso, M. (1994). Planning and Learning by Analogical Reasoning, Springer Verlag, Berlin, Germany.

W3C (2015). Semantic Web. www.w3.org/standards/semanticweb/ (accessed April 13, 2016)

Walton, D. (2004). Abductive Reasoning, University of Alabama Press, Tuscaloosa, AL.

Warden, J. A. III. (1993). Strategic Warfare: The Enemy as a System, in Mitchum, A. U. (ed.), Concepts in Airpower for the Campaign Planner, Air Command and Staff College, Maxwell AFB, AL.

Waterman, D. A., and Hayes-Roth, F. (eds.) (1978). Pattern-Directed Inference Systems, Academic Press, Orlando, FL.

Wigmore, J. H. (1913). The Problem of Proof, Illinois Law Review, vol. 8, no. 2, pp. 77–103.Google Scholar

Wigmore, J. H. (1937). The Science of Judicial Proof: As Given by Logic, Psychology, and General Experience and Illustrated in Judicial Trials, Little, Brown, Boston, MA.

Winston, P. H. (1980). Learning and Reasoning by Analogy, Communications of the ACM, vol. 23, pp. 689–703.Google Scholar

WordNet (2012). WordNet: A Database for English, Princeton University, Princeton, NJ. wordnet.princeton.edu/ (accessed August 4, 2012)

Zadeh, L. (1983). The Role of Fuzzy Logic in the Management of Uncertainty in Expert Systems, Fuzzy Sets and Systems, vol. 11, pp. 199–227.Google Scholar