Probabilistic Logic for Knowledge Representation and Automated Reasoning

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: W31P4Q-09-C-0041
Agency Tracking Number: 08ST1-0076
Amount: $98,999.00
Phase: Phase I
Program: STTR
Awards Year: 2008
Solicitation Year: 2008
Solicitation Topic Code: ST081-004
Solicitation Number: 2008.A
Small Business Information
1235 South Clark Street, Suite 400, Arlington, VA, 22202
DUNS: 036593457
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Chris Smith
 Senior Engineer
 (703) 682-1615
Business Contact
 Kelly McClelland
Title: Director, Corporate Business Office
Phone: (703) 414-5024
Research Institution
 Leslie Pack-Kaelbling
 32-G486 32 Vassar St.
Cambridge, MA, 2139
 (617) 258-9695
 Nonprofit college or university
Conventional statements of logic (e.g., simple statements of the form “if x then y”) allow individuals and machines to make quick and efficient determinations of the state of the world through the rules of deduction. This type of reasoning, however, does not naturally accommodate a fundamental and irreducible aspect of our knowledge about the world: we are more often than not uncertain about our knowledge to some degree or another. Dealing with uncertainty requires using a probabilistic representation of reasoning that allows one to express and draw inferences in cases when the facts are uncertain rather than just true or false. The Decisive Analytics Corporation/MIT (DAC/MIT) team proposes a powerful and elegant method which combines the desired expressive power of conventional logic with a sound and consistent treatment of uncertainty, resulting in an automated reasoning engine that integrates logical relations with probabilistic reasoning about complex, imprecise, and uncertain situations. The proposed hybrid inference engine will moreover be capable of hypothesizing new attributes, new relationships, and even new types of objects in its representation space and thus yield more expressive capability than other statistical relational formalisms.

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