Quantifying sequential subsumption

Quantifying the Sequential Structure of Psychotic Behavior

In its simplest form, a random reinforcement, choice task experiment is one in which the subject makes choices in an attempt to guess the outcome of a random number generator. The subject does not know that the pattern is random and will try to construct different strategies to increase the frequency of correct guesses. Differences in the pattern of choices are observed when clinical population...

Subsumption Robotics

Quantifying Uncertainty in Batch Personalized Sequential Decision Making

As the amount of data collected from individuals increases, there are more opportunities to use it to offer personalized experiences (e.g., using electronic health records to offer personalized treatments). We advocate applying techniques from batch reinforcement learning to predict the range of effectiveness that policies might have for individuals. We identify three sources of uncertainty and...

Quantifying Opportunity Costs in Sequential Transportation Auctions for Truckload Acquisition

The principal focus of this research is to quantify opportunity costs in sequential transportation auctions. This paper focuses on the study a transportation marketplace with time-sensitive truckload pickup-and-delivery requests. In this paper, two carriers compete for service requests; each arriving service request triggers an auction where carriers compete with each other to win the right of ...

Explaining Subsumption and Patching Non-Subsumption with Tableaux Methods

Explaining Subsumption and Unsatisfiability Our approach follows the idea of adapting a tableaux algorithm for explaining subsumption and unsatisfiability as suggested in [1, 4]. The explanation follows the proof and uses the structure of a tableaux tree for the production of natural-language explanations. Our method covers the DL SHIN including GCIs. We rely on a data structure for annotated t...