Showing 1 - 10 of 37 Items

Date: 2019-01-01

Creator: Sarah Harmon, Katie McDonough

Access: Open access

Prior work investigating student perceptions of scientists has revealed commonly-held beliefs, stereotypes, and even connections to career choices. We adapt the “Draw-A-Scientist” instrument to examine how undergraduates depict computational creativity researchers and the field of computational creativity as a whole. Our results indicate that there are significant differences when students are asked to draw or describe a computer scientist versus a computational creativity researcher. Whether the student is an upper-level or introductory computer science student appears to also influence responses.

Date: 2012-01-01

Creator: William K. Richard, Stephen M. Majercik

Access: Open access

Date: 2005-12-01

Creator: Stephen M. Majercik, Byron Boots

Access: Open access

We present DC-SSAT, a sound and complete divide-and-conquer algorithm for solving stochastic satisfiability (SSAT) problems that outperforms the best existing algorithm for solving such problems (ZANDER) by several orders of magnitude with respect to both time and space. DC-SSAT achieves this performance by dividing the SSAT problem into subproblems based on the structure of the original instance, caching the viable partial assignments (VPAs) generated by solving these subproblems, and using these VPAs to construct the solution to the original problem. DC-SSAT does not save redundant VPAs and each VPA saved is necessary to construct the solution. Furthermore, DC-SSAT builds a solution that is already human-comprehensible, allowing it to avoid the costly solution rebuilding phase in ZANDER. As a result, DC-SSAT is able to solve problems using, typically, 1-2 orders of magnitude less space than ZANDER, allowing DC-SSAT to solve problems ZANDER cannot solve due to space constraints. And, in spite of its more parsimonious use of space, DC-SSAT is typically 1-2 orders of magnitude faster than ZANDER. We describe the DC-SSAT algorithm and present empirical results comparing its performance to that of ZANDER on a set of SSAT problems. Copyright © 2005, American Association for Artificial Intelligence (www.aaai.org). All rights reserved.

Date: 2012-01-01

Creator: Stephen M. Majercik

Access: Open access

Swarm intelligence can provide robust, adaptable, scalable solutions to difficult problems. The distributed nature of swarm activity is the basis of these desirable qualities, but it also prevents swarm-based techniques from having direct access to global knowledge that could facilitate the task at hand. Our experiments indicate that a swarm system can use an auxiliary swarm, called a communication swarm, to create and distribute an approximation of useful global knowledge, without sacrificing robustness, adaptability, and scalability. We describe a communication swarm and validate its effectiveness on a simple problem.

Date: 2020-01-01

Creator: Luca Ostertag-Hill

Access: Access restricted to the Bowdoin Community

• Embargo End Date: 2026-05-18

Date: 2023-01-01

Creator: Evan Albers

Access: Embargoed

Date: 2022-01-01

Creator: Daniel Little

Access: Open access

In the recent past, extensive experimental works have been performed to predict joint voting outcomes in Congress based on a game-theoretic model of voting behavior known as Linear Influence Games. In this thesis, we improve the model selection and evaluation procedure of these past experiments. First, we implement two methods, Nested Cross-Validation with Tuning (Nested CVT) and Bootstrap Bias Corrected Cross-Validation (BBC-CV), to perform model selection and evaluation with less bias than previous methods. While Nested CVT is a commonly used method, it requires learning a large number of models; BBC-CV is a more recent method boasting less computational cost. Using Nested CVT and BBC-CV we perform not only model selection but also model evaluation, whereas the past work was focused on model selection alone. Second, previously models were hand picked based on performance measures gathered from CVT, but both Nested CVT and BBC-CV necessitate an automated model selection procedure. We implement such a procedure and compare its selections to what we otherwise would have hand picked. Additionally, we use sponsorship and cosponsorship data to improve the method for estimating unknown polarity values of bills. Previously, only subject code data was used. This estimation must be done when making voting outcome predictions for a new bill as well as measuring validation or testing errors. We compare and contrast several new methods for estimating unknown bill polarities.

Date: 2023-01-01

Creator: Lily Caroline Smith

Access: Access restricted to the Bowdoin Community

Date: 2017-05-01

Creator: Dominick Sanchez

Access: Open access

Particle Swarm Optimization (PSO) is often used for optimization problems due to its speed and relative simplicity. Unfortunately, like many optimization algorithms, PSO may potentially converge too early on local optima. Using multiple neighborhoods alleviates this problem to a certain extent, although premature convergence is still a concern. Using dynamic topologies, as opposed to static neighborhoods, can encourage exploration of the search space at the cost of exploitation. We propose a new version of PSO, Dynamic-Static PSO (DS-PSO) that assigns multiple neighborhoods to each particle. By using both dynamic and static topologies, DS-PSO encourages exploration, while also exploiting existing knowledge about the search space. While DS-PSO does not outperform other PSO variants on all benchmark functions we tested, its performance on several functions is substantially better than other variants.

Date: 2013-11-18

Creator: Stephen M. Majercik

Access: Open access

In the Particle Swarm Optimization (PSO) algorithm, the expense of evaluating the objective function can make it difficult, or impossible, to use this approach effectively; reducing the number of necessary function evaluations would make it possible to apply the PSO algorithm more widely. Many function approximation techniques have been developed that address this issue, but an alternative to function approximation is function conservation. We describe GREEN-PSO (GR-PSO), an algorithm that, given a fixed number of function evaluations, conserves those function evaluations by probabilistically choosing a subset of particles smaller than the entire swarm on each iteration and allowing only those particles to perform function evaluations. The "surplus" of function evaluations thus created allows a greater number of particles and/or iterations. In spite of the loss of information resulting from this more parsimonious use of function evaluations, GR-PSO performs as well as, or better than, the standard PSO algorithm on a set of six benchmark functions, both in terms of the rate of error reduction and the quality of the final solution.