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Research in Knowledge Representation, Reasoning, and Acting

Research in Knowledge Representation, Reasoning, and Acting. Stuart C. Shapiro Professor, CSE Affiliated Professor, Linguistics, Philosophy Director, Center for Cognitive Science Director, SNePS Research Group ACM Distinguished Scientist Fellow, AAAI Faculty Member:

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Research in Knowledge Representation, Reasoning, and Acting

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  1. Research inKnowledge Representation, Reasoning, and Acting Stuart C. Shapiro Professor, CSE Affiliated Professor, Linguistics, Philosophy Director, Center for Cognitive Science Director, SNePS Research Group ACM Distinguished Scientist Fellow, AAAI Faculty Member: Interdisciplinary MS in Computational Linguistics Center for MultiSource Information Fusion National Center for Ontological Research National Center for Geographic Information and Analysis

  2. Long-Term Goal • Theory and Implementation of Natural-Language-Competent Computerized Cognitive Agent/Robot • and Supporting Research in Artificial Intelligence Cognitive Science Computational Linguistics. S. C. Shapiro

  3. Cassie • A computational cognitive agent • Embodied in hardware • or software-simulated • Based on SNePS and (M)GLAIR. S. C. Shapiro

  4. MGLAIR Agent Architecture Mind KL (SNePS) Independent of lower-body implementation Body PMLa PMLb Dependent on lower-body implementation I/P s o c k e t s PMLc Proprioception Speech W O R L D Hearing SAL Vision Motion S. C. Shapiro

  5. SNePS SNePS is a Logic-Based Frame-Based Network-Based knowledge representation, reasoning, and acting system. S. C. Shapiro

  6. Some Important SNePS Features • First-person beliefs • Not third-person “truth” about agent or world • Beliefs are current beliefs • Even if about the past • On-line acting • Reified propositions as well as acts & … • Neither states nor times are privileged S. C. Shapiro

  7. Some Recent & CurrentResearch Projects • A General Characterization of Answers to Questions • KRR for Information Fusion for Cyber Security • Information Extraction for Soft Target Exploitation and Fusion • Ontological reasoning for GeoCLEF: multilingual geographic information retrieval • Intermedia Performance Studio • Actor-agents in VR drama S. C. Shapiro

  8. A General Characterization of Answers to Questions • Every clause descended from a query clause in resolution theorem proving is an answer to the query. • General form of an answer: [H ] [G ] Q H is Hypothetical component (optional) G is Generic component (optional) Q is Question component (either generic or specific) • Example: {~Cat(Boots), ~Tuna(x), Answer(Likes(Boots, x))} Cat(Boots)  x (Tuna(x)  Likes (Boots, x)) If Boots is a cat, then Boots likes to eat any tuna. [D. T. Burhans & S. C. Shapiro, Defining Answer Classes Using Resolution Refutation, Journal of Applied Logic 5,1 (March 2007), in press.] S. C. Shapiro

  9. KRR forInformation Fusionfor Cyber Security • Use SNePS to reason about computer networks, and about potential, and actual attacks. • Provide ontology-oriented SNePS GUI • A task of the National Center for Multisource Information Fusion • With Moises Sudit (IE & CMIF) & Michael Kandefer (CSE: RA) • Funded by the Air Force Research Laboratory (AFRL) S. C. Shapiro

  10. SNePS Ontology GUI • SNePS GUI supports the loading and exporting of SNePS files in several formats. • Allows the display of binary predicates as a tree hierarchy S. C. Shapiro

  11. Selecting a Predicatefor the Tree View • The user selects a predicate from the drop down menu, after which the tree is generated S. C. Shapiro

  12. Class Hierarchy • This view shows a small class hierarchy for hosts on a network S. C. Shapiro

  13. Part Of • This view shows a part of hierarchy. S. C. Shapiro

  14. Network View • Shows the network representation (drawn using the JUNG network visualization tool) of a SNePS belief base • User can zoom, move nodes, and save images as JIMI supported file types (jpg, bmp, png) • Tooltips provide information about the propositions expressed by the nodes in the network • Ex. This view shows assertions about the connectivity of various hosts (h0-h4) S. C. Shapiro

  15. Information ExtractionforSoft Target Exploitation and Fusion • Review relevant NLP tools. • Translate unstructured text messages to structured summaries. • E.g. From 03/17/07 Sabah Dulaimi arrived at 1035. To (m2 (date (m1 (day 17) (month 03) (year 07)))) (m3! (act arrive) (agent Sabah Dulaimi) (time 1035)) • With Shane Axtell (CL: RA) • Funded by the US Army CERDEC (Communications-Electronics Research, Development and Engineering Center) S. C. Shapiro

  16. Ontological Reasoningfor GeoCLEF • Provide additional search terms to retrieve articles for queries such as International sports competitions in the Ruhr area and provide means of eliminating irrelevant answers. • With Miguel Ruiz (LIS), June Abbas (LIS), Thomas Bittner (PHI & GEO), & David Mark (GEO). S. C. Shapiro

  17. Intermedia Performance Studio • Buffalo-Region resource focusing on the integration of live actors, virtual avatars, intelligent actor-agents, dynamic sets and live, mobile audience members. • With Sarah Bay-Cheng (THD & DMS), Josephine Anstey (DMS), David Pape (DMS), & Jon Bona (CSE: RA) • Funded by UB Provost’s Interdisciplinary Research Development Fund (IRDF) • See http://vrstudio.buffalo.edu/ips/wiki/ S. C. Shapiro

  18. The Trial The Trail • Virtual Reality Drama with SNePS/GLAIR agent-actors. • With Josephine Anstey (DMS), David Pape (DMS), and CSE grads: Jon Bona, Albert Goldfain, Mike Kandefer, Vishwac Sena Kannan, Madhumitha Nagarajan S. C. Shapiro

  19. The Trial The Trail Bad guy agents hassling a human participant S. C. Shapiro

  20. For More Information • Shapiro:http://www.cse.buffalo.edu/~shapiro/ • SNePS Research Group:http://www.cse.buffalo.edu/sneps/ • Meets Tuesdays 10-12, 242 Bell Hall • Join us! • Register for CSE563 in Spring, 2007 S. C. Shapiro

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