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Fall 2012

Pang, Bo

This thesis consists of two parts. First, we invented an abstraction framework called multimapping which allows multiple admissible heuristic values to be extracted from one abstract space. The key idea of this technique is to design a multimapping function which maps one state in the original...

Fall 2013

Nakhost, Hootan

This thesis introduces random walk (RW) planning as a new search paradigm for satisficing planning by studying its theory, its practical relevance, and applications. We develop a theoretical framework that explains the strengths and weaknesses of random walks as a tool for heuristic search....

Fall 2017

Barriga Richards, Nicolas A

Real-time strategy (RTS) games are war simulation video games in which the players perform several simultaneous tasks like gathering and spending resources, building a base, and controlling units in combat against an enemy force. RTS games have recently drawn the interest of the game AI research...

Fall 2021

Abdullah

Traffic congestion is a severe problem in many cities. One way to reduce it is by optimizing traffic signal timings. Experts spend a lot of time analyzing traffic patterns to produce good handcrafted timing schedules. However, these timing schedules can be less responsive when there is a sudden...

Fall 2023

Stevens, Justin D

The Witness is a game with difficult combinatorial puzzles that are challenging for both human players and artificial intelligence based solvers. Indeed, the number of candidate solution paths to the largest puzzle considered in this thesis is on the order of 10^(15) and search-based solvers can...

Fall 2019

Fan, Gaojian

In this thesis, we study merge-and-shrink (M&S), a flexible abstraction technique for generating heuristics for cost optimal planning. We first propose three novel merging strategies for M&S, namely, Undirected Min-Cut (UMC), Maximum Intermediate Abstraction Size Minimizing (MIASM), and Dynamic...

Spring 2016

Morrill, Dustin R

Game theoretic solution concepts, such as Nash equilibrium strategies that are optimal against worst case opponents, provide guidance in finding desirable autonomous agent behaviour. In particular, we wish to approximate solutions to complex, dynamic tasks, such as negotiation or bidding in...