Title

Evolutionary Computation for the Automated Design of Puzzle Instances for Artificial Intelligence Education

Presenter Information

Joseph Szatkowski

Department

Computer Science

Major

Computer Science and Computer Engineering

Research Advisor

Tauritz, Daniel R.

Advisor's Department

Computer Science

Abstract

High-quality Artificial Intelligence (AI) education goes beyond traditional lectures by stimulating students’ desire to learn more deeply through Problem Based Learning (PBL). A typical approach in introductory AI courses involves providing the students’ challenging puzzles and games which capture the algorithmic complexity necessary to deal with real-world problem solving without overloading the students with having to deal with the messy details and scale of the real-world. However, it takes very significant effort to manually create such puzzles and games that are both intellectually stimulating and appropriate for the AI algorithms being taught. A critical component of puzzle design is creating a sequence of puzzle instances which differentiate the solving power of aforementioned AI algorithms. This project is concerned with automating the design of puzzle instances employing evolutionary computation in order to provide a high-quality hands-on learning experience.

Biography

Joseph Szatkowski is a senior pursuing degrees in Computer Science and Computer Engineering. He has previously taken classes in AI and Evolutionary Computing.

Research Category

Sciences

Presentation Type

Poster Presentation

Document Type

Poster

Location

Upper Atrium/Hall

Start Date

4-11-2017 9:00 AM

End Date

4-11-2017 11:45 AM

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Apr 11th, 9:00 AM Apr 11th, 11:45 AM

Evolutionary Computation for the Automated Design of Puzzle Instances for Artificial Intelligence Education

Upper Atrium/Hall

High-quality Artificial Intelligence (AI) education goes beyond traditional lectures by stimulating students’ desire to learn more deeply through Problem Based Learning (PBL). A typical approach in introductory AI courses involves providing the students’ challenging puzzles and games which capture the algorithmic complexity necessary to deal with real-world problem solving without overloading the students with having to deal with the messy details and scale of the real-world. However, it takes very significant effort to manually create such puzzles and games that are both intellectually stimulating and appropriate for the AI algorithms being taught. A critical component of puzzle design is creating a sequence of puzzle instances which differentiate the solving power of aforementioned AI algorithms. This project is concerned with automating the design of puzzle instances employing evolutionary computation in order to provide a high-quality hands-on learning experience.