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Edward Katz

Associate Teaching Professor - Silicon Valley

Contact

Eed.katz@northeastern.edu

Office Location

Northeastern University - Silicon Valley
6024 Silver Creek Valley Rd
San Jose, CA 95138

Mailing Address

Northeastern University
ATTN: Ed Katz, 6024 Silver Creek Valley Rd
360 Huntington Avenue
Boston, MA 02115

Research Interests

  • Robotic software systems (primarily autonomous mobile robotics)
  • Computational intelligence (primarily Fuzzy Logic)
  • Smart environments and software agents
  • Multi-agent systems (rule-based, knowledge-based, and model-based)
  • Computation
  • Traditional Artificial Intelligence (GOFAI).

Education

  • Visiting Scholar, Robotics Laboratory — Computer Department, Stanford University
  • PhD in Computer Science, University of Louisiana at Lafayette
  • MS in Computer Science, University of Missouri-Columbia
  • BS in Mathematics, Purdue University

Biography

Edward Katz is currently an Associate Teaching Professor in the M.S. Computer Science and ALIGN programs at Northeastern University Silicon Valley’s Khoury College of Computer Sciences.

Previously, he was a Visiting Associate Professor in Computer Science at Loyola Marymount University for 2016-2017. Prior to LMU, he mentored Entrepreneurial Teams of MS students at Carnegie Mellon University Silicon Valley. Before mentoring CMU SV teams, he was both a Senior Systems Scientist and an Associate Professor in Software Engineering at CMU SV. Before that, he was a Senior MTS at Hewlett-Packard Laboratories.

What are the specifics of your industry experience?

Invited to be a Visiting Scholar at the Stanford University Computer Science Department’s Robotics Laboratory, I collaborated with legendary AI Pioneer Prof. Nils J. Nilsson. One of the results was my developing the Fuzzy Teleo-Reactive extended paradigm for autonomous robot agent control, an extension of Nilsson’s Teleo-Reactive pioneering work.

I have published technical papers in international conferences and have been awarded two US Patents. My professional memberships include Senior Member, Association for the Advancement of Artificial Intelligence (AAAI), Senior Member, Association for Computing Machinery (ACM), and Senior Member, Institute of Electrical and Electronics Engineers (IEEE). I currently am the 2018 IEEE Robotics and Automation Society Chapter Chair for the entire San Francisco Bay Area region.

Recent Publications

Generating, Executing, and Monitoring Plans with Goal-Based Utilities in Continuous Domains

Langley, P., Choi, D., Barley, M., Meadows, B., & Katz, E. P. (2017). Generating, executing, and monitoring plans with goal-based utilities in continuous domains. Proceedings of the Fifth Annual Conference on Cognitive Systems. Troy, NY.

Abstract

Like humans, autonomous agents must operate in continuous physical settings which involve competing objectives that change over time. The PUG architecture addresses these issues by combining relational and quantitative descriptions of states, associating numeric utilities to symbolic goals, and using mental simulation to evaluate alternative plans. In this paper, we report extensions to PUG that interleave plan generation with execution and monitoring. We describe each of these processes, how they interact, and how they lead to plan revision when anomalies arise. We demonstrate the extended architecture’s behavior in a continuous physical domain, including four classes of scenarios in which unexpected developments lead the agent to revise its plans before continuing execution. In closing, we review work on related topics and discuss avenues for future research.

Goals, utilities, and mental simulation in continuous planning

Langley, P., Barley, M., Meadows, B., Choi, D., & Katz, E. P. (2016). Goals, utilities, and mental simulation in continuous planning. Proceedings of the Fourth Annual Conference on Cognitive Systems. Evanston, IL.

Abstract

Like humans, autonomous agents will need to operate in physical settings that involve competing objectives which may be incompatible and vary in importance over time. They will also need to reason about both qualitative relations and quantitative attributes to produce behavior that is appropriate to the situation. In this paper, we report PUG, a problem-solving architecture that combines conceptual inference with plan generation, encodes both relational and quantitative content, and integrates symbolic goals with numeric utilities. Mental simulation plays a key role in evaluating operators, guiding search, and determining when a plan is acceptable. We describe the system’s representational assumptions and the mechanisms that operate over them, after which we demonstrate its behavior in settings that involve conflicting goals with utilities that vary over time. In closing, we discuss related work and outline our plans for future research.