Received: from GLINDA.OZ.CS.CMU.EDU by A.GP.CS.CMU.EDU id aa27700; 8 Nov 95 13:27:19 EST Date: Wed, 8 Nov 95 13:26:37 EST From: AI.Repository@GLINDA.OZ.CS.CMU.EDU To: ai+ai-postdoc@cs.cmu.edu Subject: Postdoc: Planning and Learning at Carnegie Mellon Univ. (Pittsburgh, PA) Sender: ai@A.GP.CS.CMU.EDU COMPUTER SCIENCE DEPARTMENT, CARNEGIE MELLON UNIVERSITY, PITTSBURGH, PA POSITION: Post-doctoral fellow in Computer Science CONTACT: Prof. Manuela M. Veloso Computer Science Department Carnegie Mellon University Pittsburgh, PA 15213-3891 by email: veloso@cs.cmu.edu DESCRIPTION: Two-year (possibly three-year) post-doctoral position working on planning and learning by analogical/case-based reasoning within the Planning Initiative funded by the U.S. Department of Defense. Our main focus will be to extend the derivational analogy framework developed in Prodigy/Analogy within the context of a mixed-initiative environment in which the human user and the machine planners cooperate. In particular, within the fully automated Prodigy/Analogy system, the rationale for planning decisions is captured and replayed autonomously. The envisioned extension to this approach would observe a user make planning decisions, capture the user's rationale for the choices explored and selected, provide automated support for any detailed planning necessary, accumulate the annotated mixed-initiative planning episodes, and support the user with recollection and guidance for reuse of past planning episodes and their rationale. We will be responsible for using and demonstrating the system in a realistic military application. Several faculty members are involved in this project and we will also be in close collaboration with other parties in the Planning Initiative. The post-doctoral fellow selected is expected to make significant contributions to the specific project mentioned above, as well as contribute to the intellectual life of the larger researcher community in the Computer Science Department at Carnegie Mellon University. QUALIFICATIONS: Applicants should have a Ph.D. in Computer Science. This is an Artificial Intelligence system-building position within the context of a specific application, so a very strong systems implementation background is required. Experience in planning, analogical/case-based reasoning, integrated machine-user approaches, and basic knowledge of machine learning techniques will be preferred. TO APPLY: Send a resume and names of three references to the physical or email address above (email preferred). Please have recommendations letters specifically address the system building strengths and weaknesses of the applicant. CMU is an Equal Opportunity, Affirmative Action Employer. ------------------------------------------------------------------------------- This message | Submissions ai+ai-postdoc@cs.cmu.edu was sent via | Subscribe/Unsubscribe ai+query@cs.cmu.edu the AI-POSTDOC | Available mailing lists include mailing list. | AI-JOBS, LISP-JOBS, PROLOG-JOBS, AI-POSTDOC, AI-PREDOC