Charles Ortiz

Charles Ortiz
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Main area of research
Natural language & AI

Charles Ortiz is the Senior Principal Manager of the Artificial Intelligence and Reasoning Group at the Nuance Natural Language and AI Laboratory in Sunnyvale California.  His research at Nuance focuses on collaborative dialog systems and commonsense reasoning for personal assistants. Prior to joining Nuance, he was the director of research in collaborative multi-agent systems at the AI Center at SRI International. His research interests and contributions are in multiagent systems (collaborative dialogue-structured assistants, collaborative work environments, negotiation protocols, and logic-based BDI theories), knowledge representation and reasoning (causation, counterfactuals, and commonsense reasoning), and robotics (cognitive robotics, team-based robotics, and dialogue-based human-robot interaction). He has approximately 20 years of technical leadership and management experience in leading major projects and setting strategic directions. He has collaborated extensively with faculty and students at many academic institutions including Harvard University, Bar-Ilan University, UC Berkeley, Columbia University, University of Southern California, Vassar College, and Carnegie Mellon University. He holds an S.B. in Physics from MIT, an M.S. in Computer Science from Columbia University, and a Ph.D. in Computer and Information Science from the University of Pennsylvania. Following his PhD research, he was a Postdoctoral Research Fellow at Harvard University. He has taught courses at Harvard and was also an adjunct faculty member at UC Berkeley.  He has presented numerous tutorials at technical conferences (IJCAI 1999 and 2005, AAAI 2002 and 2004, AAMAS 2002-2004).

Selected articles

Dynamic Intention Structures I: A theory of intention representation

This article introduces a new theory of intention representation which is based on a structure called a Dynamic Intention Structure (DIS). The theory of DISs

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Centibots: large-scale autonomous robotic search and rescue experiment

The Centibots were tested, by an independent evaluation team, in an artificial “search and rescue ” scenario. The key was to evaluate how to control

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Distributed multi-robot exploration, mapping, and task allocation

We present an integrated approach to multi-robot exploration, mapping and searching suitable for large teams of robots operating in unknown areas lacking an existing supporting

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Measuring the expected gain of communicating constraint information

In this paper we investigate methods for measuring the expected utility from communicating information in multi-agent planning and scheduling problems. We consider an environment where

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Quantifying the expected utility of information in multi-agent scheduling tasks

In this paper we investigate methods for analyzing the expected value of adding information in distributed task scheduling problems. As scheduling problems are NP-complete, no

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