Modeling and Solving AI Problems in Picat

by Roman Barták and Neng-Fa Zhou

Picat is a logic-based multi-paradigm language that integrates logic programming, functional programming, constraint programming, and scripting. Picat takes many features from other languages, including logic variables, unification, backtracking, pattern-matching rules, functions, list/array comprehensions, loops, assignments, tabling for dynamic programming and planning, and constraint solving with CP (constraint programming), SAT (satisfiability), and MIP (mixed integer programming).

The goal of this tutorial is to show how to declaratively model some classical AI problems, including constraint satisfaction and planning problems, and solve them efficiently using Picat. The tutorial is targeted to researchers who are interested in AI algorithms and need a high-level modeling tool for rapid prototyping and efficient solving of AI problems. The audience will learn the basics of the Picat language and its programming, and some general techniques and tricks for modeling constraint satisfaction and planning problems. Modeling has been an important topic in constraint programming and operations research. However, modeling in planning has received little attention. The major novelty of the tutorial is to show how declarative and concise models in Picat can lead to efficient solutions. Also, there will be sessions explaining how high-level models are compiled to encodings for fast solving by existing solvers.

About the Authors

	Roman Barták works as a full professor and a researcher at Charles University in Prague (Czech Republic), where he leads Constraint Satisfaction and Optimization Research Group. His research work focuses on techniques of constraint satisfaction and modeling and their application to planning, scheduling, and other areas.
	Neng-Fa Zhou is a professor of Computer and Information Science at CUNY Brooklyn College and Graduate Center. He is the principal creator of the Picat system. He is an enthusiast of problem solving methods and tools. He won prizes in several competitions, including CSP, ASP, and MiniZinc Challenge.

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