Video link: https://vimeo.com/184511491]]>

Video link: https://vimeo.com/184511491]]>

This talk will cover various aspects of Logic Programming. We examine Logic Programming in the contexts of Programming Languages, Mathematical Logic and Machine Learning. We will we start with an introduction to Prolog and metaprogramming in Prolog. We will also discuss how miniKanren and Core.Logic differ from Prolog while maintaining the paradigms of logic programming. We will then cover the Unification Algorithm in depth and examine the mathematical motivations which are rooted in Skolem Normal Form. We will describe the process of converting a statement in first order logic to clausal form logic. We will also discuss the applications of the Unification Algorithm to automated theorem proving and type inferencing. Finally we will look at the role of Prolog in the context of Machine Learning. This is known as Inductive Logic Programming. In that context we will briefly review Decision Tree Learning and it’s relationship to ILP. We will then examine Sequential Covering Algorithms for learning clauses in Propositional Calculus and then the more general FOIL algorithm for learning sets of Horn clauses in First Order Predicate Calculus. Examples will be given in both Common Lisp and Clojure for these algorithms. Pierre de Lacaze has over 20 years’ experience with Lisp and AI based technologies. He holds a Bachelor of Science in Applied Mathematics and Computer Science and a Master’s Degree in Computer Science. He is the president of LispNYC.org ]]>

This talk will cover various aspects of Logic Programming. We examine Logic Programming in the contexts of Programming Languages, Mathematical Logic and Machine Learning. We will we start with an introduction to Prolog and metaprogramming in Prolog. We will also discuss how miniKanren and Core.Logic differ from Prolog while maintaining the paradigms of logic programming. We will then cover the Unification Algorithm in depth and examine the mathematical motivations which are rooted in Skolem Normal Form. We will describe the process of converting a statement in first order logic to clausal form logic. We will also discuss the applications of the Unification Algorithm to automated theorem proving and type inferencing. Finally we will look at the role of Prolog in the context of Machine Learning. This is known as Inductive Logic Programming. In that context we will briefly review Decision Tree Learning and it’s relationship to ILP. We will then examine Sequential Covering Algorithms for learning clauses in Propositional Calculus and then the more general FOIL algorithm for learning sets of Horn clauses in First Order Predicate Calculus. Examples will be given in both Common Lisp and Clojure for these algorithms. Pierre de Lacaze has over 20 years’ experience with Lisp and AI based technologies. He holds a Bachelor of Science in Applied Mathematics and Computer Science and a Master’s Degree in Computer Science. He is the president of LispNYC.org ]]>

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This is a presentation given January 2013 at the 7-Languages-in-7-Months New York meetup group.]]>

This is a presentation given January 2013 at the 7-Languages-in-7-Months New York meetup group.]]>

These are my slides from a mini Clojure tutorial presented at the "7 Languages in 7 Months" meetup group. The first part of the presentation faithfully presents material from Bruce Tate book, and the second part covers the more advanced topics of state management and macros]]>

These are my slides from a mini Clojure tutorial presented at the "7 Languages in 7 Months" meetup group. The first part of the presentation faithfully presents material from Bruce Tate book, and the second part covers the more advanced topics of state management and macros]]>

This paper describes BABAR, a knowledge extraction and representation system, completely implemented in CLOS, that is primarily geared towards organizing and reasoning about knowledge extracted from the Wikipedia Website. The system combines natural language processing techniques, knowledge representation paradigms and machine learning algorithms. BABAR is currently an ongoing independent research project that when sufficiently mature, may provide various commercial opportunities. BABAR uses natural language processing to parse both page name and page contents. It automatically generates Wikipedia topic taxonomies thus providing a model for organizing the approximately 4,000,000 existing Wikipedia pages. It uses similarity metrics to establish concept relevancy and clustering algorithms to group topics based on semantic relevancy. Novel algorithms are presented that combine approaches from the areas of machine learning and recommender systems. The system also generates a knowledge hypergraph which will ultimately be used in conjunction with an automated reasoner to answer questions about particular topics. ]]>

This paper describes BABAR, a knowledge extraction and representation system, completely implemented in CLOS, that is primarily geared towards organizing and reasoning about knowledge extracted from the Wikipedia Website. The system combines natural language processing techniques, knowledge representation paradigms and machine learning algorithms. BABAR is currently an ongoing independent research project that when sufficiently mature, may provide various commercial opportunities. BABAR uses natural language processing to parse both page name and page contents. It automatically generates Wikipedia topic taxonomies thus providing a model for organizing the approximately 4,000,000 existing Wikipedia pages. It uses similarity metrics to establish concept relevancy and clustering algorithms to group topics based on semantic relevancy. Novel algorithms are presented that combine approaches from the areas of machine learning and recommender systems. The system also generates a knowledge hypergraph which will ultimately be used in conjunction with an automated reasoner to answer questions about particular topics. ]]>

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