Two papers authored by computer scientists from Harvey Mudd College Professor George Monta\u00f1ez\u2019s AMISTAD Lab have been accepted to the 14th International Conference on Agents and Artificial Intelligence (ICAART 2022), Feb. 3\u20135. Both papers were accepted as\u00a0full papers\u00a0for\u00a0oral presentation\u00a0in the main\u00a0conference track.<\/p>\n
\u201cFor previous iterations of ICAART, the full paper acceptance rate has been less than 20%,\u201d says Monta\u00f1ez, \u201cso getting a full paper in that track is an accomplishment. The selection for oral presentation is an additional honor.\u201d<\/p>\n
\u201cVectorization of Bias in Machine Learning Algorithms\u201d by Sophie Bekerman \u201924, Eric Chen \u201924 and Lily Lin (Biola University) concerns estimation of inductive orientation vectors, which have, until now, been a strictly theoretical quantity used for proving various bounds on algorithm performance. \u201cThis work presents methods for estimating these vectors from data, allowing us to cluster and compare black-box classification algorithms based on their output behavior alone,\u201d Monta\u00f1ez says. \u201cSince the vectors reflect underlying assumptions and biases in the algorithms, it allows us to find similarities in the inductive biases of algorithms without knowing anything about their internal structure.\u201d<\/p>\n
\u201cThe Gopher Grounds: Testing the Link Between Structure and Function in Simple Machines\u201d by Anshul Kamath \u201923, Nick Grisanti \u201923, Sadie Zhao \u201923 (Pomona College) and Monta\u00f1ez builds off a 2021 paper (\u201cThe Gopher\u2019s Gambit: Survival Advantages of Artifact-Based Intention Perception\u201d by Monta\u00f1ez, Cynthia Hom \u201923, Amani Maina-Kilaas \u201923, Kevin Ginta \u201921 [Biola University] and Cindy Lay \u201922 [Claremont McKenna College]) on intention-perception in traps for artificial gophers. \u201cIn this new paper, we test and challenge some of the assumptions in the original paper (such as whether coherence is strongly or only weakly correlated with functionality) and see if producing traps using genetic algorithms changes the properties of the produced traps in terms of their coherence and functionality,\u201d says Monta\u00f1ez. This paper is part of a larger body of work that includes a third paper still in development, looking specifically at applying the hypothesis tests from the Hom et al. paper to the genetic-algorithm produced traps.<\/p>\n","protected":false},"excerpt":{"rendered":"
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