ACRL EXHIBIT BOOTH

16-899C ACRL Tetris Reinforcement Learner

Our approach to this problem was to use reinforcement learning with a function approximator to approximate the state value function [RSS98]. In our case, a +1 reward was given for every completed line, so that the value function would encode the long-term number of lines that is going to be completed by the algorithm. In order to achieve this, we extract features from the game state, and use gr...

The Voting Booth Catherine

The then-modern panopticon, in its subtle and unseen exercise of voyeurism and power, was to be sure an improvement over the crowded, stale prisons that preceded it. Which already incarcerated individual would not give up some personal freedom for such improved conditions? The same line of reasoning is used in modern surveillance systems. Just last week, in a New York Times article on Chicago’s...

Dirk Zeller Æ Shawn Booth

Fisheries catches from Pacific Island coral reefs are rarely recorded in official statistics. Reconstruction of catch estimates with limited hard data requires interpolation and assumptions, justifiable only by the unsatisfactory alternative of continued substitution of zero catches, a common policy interpretation for ‘no data’. Uncertainties associated with reconstructions are high, requiring ...

New Self-Checking Booth Multipliers

This work presents the first self-checking Booth-3 multiplier and a new self-checking Booth-2 multiplier using parity prediction. We propose a method which combines error-detection of Booth-3 (or Booth-2) decoder cells and parity prediction. Additionally, code disjointness is ensured by reusing logic for partial product generation. Parity prediction is applied to a carry-save-adder with the sta...

Laurence Booth Professor of Finance