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Skip to Search Results- 12Heuristic Search
- 4Artificial Intelligence
- 4Planning
- 3Machine Learning
- 2Computing Science
- 1AI planning
- 1Abdullah
- 1Barriga Richards, Nicolas A
- 1Fan, Gaojian
- 1Jabbari Arfaee, Shahab
- 1Kohankhaki, Farnaz
- 1Mohammad Lavasani, Sepehr
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Spring 2024
In recent years, significant strides in optimal bidirectional heuristic search (Bi-HS) have deepened our theoretical understanding and boosted performance. Yet, algorithms for Bi-HS in unbounded suboptimal scenarios remains largely unexplored. Despite leveraging front-to-end (F2E) and...
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Fall 2010
We investigate the use of machine learning to create effective heuristics for single-agent search. Our method aims to generate a sequence of heuristics from a given weak heuristic h{0} and a set of unlabeled training instances using a bootstrapping procedure. The training instances that can be...
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Fall 2013
Many important problems can be cast as state-space problems. In this dissertation we study a general paradigm for solving state-space problems which we name Cluster-and-Conquer (C&C). Algorithms that follow the C&C paradigm use the concept of equivalent states to reduce the number of states...
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Fall 2014
Heuristic search has been shown to be an effective way to solve state-space problems. While many heuristic search techniques are guaranteed to find the best solution, these are often not feasible given practical resource requirements. In such cases, it is necessary to sacrifice solution...
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Spring 2016
This thesis proposes, analyzes and tests different exploration-based techniques in Greedy Best-First Search (GBFS) for satisficing planning. First, we show the potential of exploration-based techniques by combining GBFS and random walk exploration locally. We then conduct deep analysis on how...
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Fall 2017
Modern board, card, and video games are challenging domains for AI research due to their complex game mechanics and large state and action spaces. For instance, in Hearthstone — a popular collectible card (CC) (video) game developed by Blizzard Entertainment — two players first construct their...
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Fall 2022
Monte Carlo Tree Search (MCTS) is a popular tree search framework for choos- ing actions in decision-making problems. MCTS is traditionally applied to applications in which a perfect simulation model is available. However, when the model is imperfect, the performance of MCTS drops heavily. In...
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Fall 2013
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....
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Fall 2017
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...
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Fall 2021
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...