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Subject offerings of new and developing areas of knowledge in industrial engineering intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: 1 to 4 undergraduate hours. 1 to 4 graduate hours. May be repeated in the same or separate terms if topics vary to a maximum of 9 hours.

Prerequisite: IE 300 or equivalent probability and statistics course; IE 310 or equivalent optimization course; MATH 257 or equivalent linear algebra course. Description: Explores the intersection of artificial intelligence and game theory with applications to markets and multi-agent systems. Covers fundamentals of AI and game theory, then examines three critical intersections: (1) AI for Game Solving?scalable methods for equilibrium computation and solving large-scale imperfect-information games; (2) AI in Markets?how learning algorithms create economic value, with applications to recommendation systems; and (3) Game Theory for Multi-Agent AI?how strategic incentives and mechanism design can improve the safety, robustness, and efficiency of autonomous systems, including the detection and prevention of algorithmic collusion.