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Skip to Search Results- 11Heuristic Search
- 6Abstractions
- 6Artificial Intelligence
- 4Planning
- 3Machine Learning
- 2Computing Science
- 1Abdullah
- 1Barriga Richards, Nicolas A
- 1Fan, Gaojian
- 1Hawkin, John A
- 1Jabbari Arfaee, Shahab
- 1Kohankhaki, Farnaz
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Fall 2009
For zero-sum games, we have efficient solution techniques. Unfortunately, there are interesting games that are too large to solve. Here, a popular approach is to solve an abstract game that models the original game. We assume that more accurate the abstract games result in stronger strategies....
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Spring 2011
In this thesis, we study theoretically and empirically the additive abstraction-based heuristics. First we present formal general definitions for abstractions that extend to general additive abstractions. We show that the general definition makes proofs of admissibility, consistency, and...
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Fall 2014
An agent in an adversarial, imperfect information environment must sometimes decide whether or not to take an action and, if they take the action, must choose a parameter value associated with that action. Examples include choosing to buy or sell some amount of resources or choosing whether or...
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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 2012
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...