We propose the k-representative regret minimization query (k-regret) as an operation to support multi-criteria decision making. Like top-k, the k-regret query assumes that users have some utility or scoring functions; however, it never asks the users to provide such functions. Like skyline, it filters out a set of interesting points from a potentially large database based on the users’ criteria...

When deciding when to make a purchase, people often compare their outcomes to those that would have occurred had they purchased earlier or later. In this article, we examine how preand postpurchase comparisons affect regret and satisfaction, and whether consumers learn to avoid decisions that result in regret. In the first two experiments, we show that information learned after the purchase has...

Motivations I “Flat” RL : difficult to learn complex behaviours (eg, sequence of subgoals) ⇒ Humans abstract from low-level actions I Hierarchical RL : decompose large problems into smaller ones by imposing constraints on value function or policy I Possible implementation: options [Sutton et al., 1999] I Empirical observations: introducing options in an MDP can speed up learning but can also be...

Internet routing is mostly based on static information— it’s dynamicity is limited to reacting to changes in topology. Adaptive performance-based routing decisions would not only improve the performance itself of the Internet but also its security and availability. However, previous approaches for making Internet routing adaptive based on optimizing network-wide objectives are not suited for an...

In large extensive form games with imperfect information, Counterfactual Regret Minimization (CFR) is a popular, iterative algorithm for computing approximate Nash equilibria. While the base algorithm performs a full tree traversal on each iteration, Monte Carlo CFR (MCCFR) reduces the per iteration time cost by traversing just a sampled portion of the tree. On the other hand, MCCFR’s sampled v...

This paper focuses on the optimal allocation of multi-stage attacks with uncertainty in attacker’s intention. We model attack planning problem using a Markov decision process and characterize intention finite set reward functions–each represents type attacker. Based this modeling, we employ paradigm worst-case absolute regret minimization from robust game theory develop mixed-integer linear pro...

No-regret learning has emerged as a powerful tool for solving extensive-form games. This was facilitated by the counterfactual-regret minimization (CFR) framework, which relies on the instantiation of regret minimizers for simplexes at each information set of the game. We use an instantiation of the CFR framework to develop algorithms for solving behaviorally-constrained (and, as a special case...