WiMLDS's mission is to support and promote women and gender minorities who are practicing, studying or are interested in the fields of machine learning and data science. We create opportunities for members to engage in technical and professional conversations in a positive, supportive environment by hosting talks by women and gender minority individuals working in data science or machine learning. Events include technical workshops, networking events and hackathons. We are inclusive to anyone who supports our cause regardless of gender identity or technical background.
Howard A. Blair EECS Dept., 2-177 SciTech Syracuse University Syracuse, NY 13210 USA Introduction Solutions to problems are often not unique. A representation of a problem as a theory in some logical formalism often admits a number of models representing solutions (Marek Truszczyfiski, 1999). The solution-representing models are perhaps required to come from a particular class. The typical strategy is to represent a class of problem instances E, for example the problem of determining a Hamiltonian circuit in a digraph if there is one, as a theory TE, and a specific instance I of E, e.g. a specific digraph, as a theory T1 in such a way that certain kinds of models of the combined theory TE TI represent the solutions, i.e. in the example, the required Hamiltonian circuits in I, as the result of a mapping from answer sets (the models) solutions of I. As a specific formalism for answer set programming, DATALOG programs with negation (Ceri, Gottlob, & Tanka, 1990) and their stable models have received a large amount of attention e.g.