Project "Planning with Realistic Preference Models"
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Deterministic search methods typically associate a cost with every edge of a graph and attempt to minimize the cost of achieving a given goal vertex. Similarly, probabilistic search methods typically either maximize the probability or minimize the expected cost of achieving a given goal vertex. However, human decision makers often have more complex preference models, and planning systems need to adopt these objective functions to be helpful for their users. For example, human decision makers often trade off between different kinds of cost. They are also often risk-averse in high-stake one-shot decision situations under uncertainty, such as planning for crisis situations (for example, oil spills) or space applications.

While AI researchers had concentrated on how to search efficiently for more realistic (and thus larger) world models, my collaborators and I have concentrated on how to exploit the structure of deterministic and probabilistic planning problems to search efficiently for preference models that allow one to model the preferences of human decision makers better than the traditional ones. For example, we have also developed fast dynamic programming methods that maximize the expected utility for Markov decision problems with non-linear utility functions, such as an exact backward induction method for one-switch utility functions and several variants of approximate functional value iteration for arbitrary utility functions. These dynamic programming methods manipulate concisely represented functions from wealth levels to values. Overall, we have developed search methods for sensor planning, planning with risk-attitudes for high-stake one-shot decision situations, and planning for deadlines and other real-valued scarce resources, such as energy. I received an NSF CAREER award on this topic.

Currently, we are working on speeding up deterministic bi- and multi-objective search since many applications need to tradeoff between two (or more) competing resources, such as travel time and energy. Examples include solving transportation problems, planning power-transmission lines, scheduling satellites and routing packets in computer networks.

News

We gave a tutorial on multi-objective search at AAAI 2024.

We had a multi-objective research meeting on December 6, 2024 at the University of Southern California with in-person presence from the Technion, Ben Gurion University, and the University of Southern California in the context of a joint research project funded by the US National Science Foundation (NSF) and the US-Israel Binational Science Foundation (BSF).

We had a multi-objective research retreat on December 12-15, 2023 at the Technion (Israel) with in-person presence from the University of Southern California, Universidad San Sebastian, Washington University in St. Louis, the Technion, and Ben Gurion University in the context of a joint research project funded by the US National Science Foundation (NSF) and the US-Israel Binational Science Foundation (BSF).

Representative Overview Publications

Representative Publications on Multi-Attribute Planning

Representative Publications on High-Stake Planning

Representative Publications on Hard or Soft Deadlines

Dissertations

Some of this material is based upon work supported by the National Science Foundation under Grant Nos. 9984827 and 2121028 as well as an IBM Faculty Fellowship. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or the other sponsoring organizations.

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This page was automatically created by a bibliography maintenance system that was developed as part of an undergraduate research project, advised by Sven Koenig.

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