Making Rational Decisions Using Adaptive Utility Elicitation

By Urszula Chajewska, Daphne Koller, Ronald E. Parr. AAAI/IAAI, 2000

345 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/39bb60618428e664c0f171c1658985fb40ac135b

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Citations in the corpus

A POMDP formulation of preference elicitation problems (2002, methodology): “The chief exception is the work of Chajewska, Koller, and Parr[6], who model this problem by assuming a distribution over utility functions, and refining uncertainty over a user’s utility as queries are answered.”
Towards Resolving Unidentifiability in Inverse Reinforcement Learning (2016, background): “In the economics literature, the problem of inferring an agent’s utility from behavior has long been studied under the heading of utility or preference elicitation [3, 19, 15, 16, 13, 15].”
Quantilizers: A Safer Alternative to Maximizers for Limited Optimization (2016, background): “…the expectation of some utility function, as seen in the works of Heckerman and Horvitz [1], Bacchus and Grove [2], and Chajewska, Koller, and Parr [3]. von Neumann and Morgenstern [4] provide compelling arguments that any rational agent with consistent preferences must act as if it is maximizing…”
Live Video Analytics at Scale with Approximation and Delay-Tolerance (2017, background): “Utility elicitation [28, 30, 35] helps obtain the exact shape of the utility function.”
Label ranking by learning pairwise preferences (2008, background): “Chajewska et al. (2000) accomplish learning of a utility function by treating utility as a random variable.”
How to Design AI for Social Good: Seven Essential Factors (2020, background): “The balancing problem, which is familiar to preference-elicitation research (Bou-tilier 2002; Faltings et al. 2004; Chajewska et al. 2000), boils down to a temporal choice interdependency.”
Repeated Inverse Reinforcement Learning (2017, background): “In the economics literature, the problem of inferring an agent’s utility from behavior has long been studied under the heading of utility or preference elicitation (Chajewska et al., 2000; Von Neumann & Morgenstern, 2007; Regan & Boutilier, 2009; 2011; Rothkopf & Dimitrakakis, 2011).”
Gaussian Process Preference Elicitation (2010, unknown): “In this paper we focus on pairwise comparison queries for PE, which are known to have low cognitive load [3, 4].”
Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making (2018, methodology): “For each run, we first randomly (uniformly) choose the d utility functions fi we use, as well as the values a ∈ [10, 50], b ∈ [1, 20], n ∈ [1, 10] and c ∈ [1, 5] (separately for each fi ).”
Comparing Action-Query Strategies in Semi-Autonomous Agents (2011, background): “Our problem is related to preference elicitation in the presence of utility uncertainty (Chajewska, Koller, and Parr 2000).”
Autonomous agents modelling other agents: A comprehensive survey and open problems (2017, methodology): “Closely related areas are active learning [242], preference elicitation [45,69], and Bayesian experimental design [73].”
Deep Learning for Reward Design to Improve Monte Carlo Tree Search in ATARI Games (2016, background): “Yet others have explored the use of queries to a human expert to elicit preferences [ Chajewska et al. , 2000 ] .”