Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Valenzano, Richard"'
Expected Runtime Comparisons Between Breadth-First Search and Constant-Depth Restarting Random Walks
When greedy search algorithms encounter a local minima or plateau, the search typically devolves into a breadth-first search (BrFS), or a local search technique is used in an attempt to find a way out. In this work, we formally analyze the performanc
Externí odkaz:
http://arxiv.org/abs/2406.16697
Microplastic particle ingestion or inhalation by humans is a problem of growing concern. Unfortunately, current research methods that use machine learning to understand their potential harms are obstructed by a lack of available data. Deep learning t
Externí odkaz:
http://arxiv.org/abs/2404.07356
This paper presents an approach that evaluates best-first search methods to code refactoring. The motivation for code refactoring could be to improve the design, structure, or implementation of an existing program without changing its functionality.
Externí odkaz:
http://arxiv.org/abs/2305.07594
Autor:
Icarte, Rodrigo Toro, Waldie, Ethan, Klassen, Toryn Q., Valenzano, Richard, Castro, Margarita P., McIlraith, Sheila A.
Reinforcement learning (RL) is a central problem in artificial intelligence. This problem consists of defining artificial agents that can learn optimal behaviour by interacting with an environment -- where the optimal behaviour is defined with respec
Externí odkaz:
http://arxiv.org/abs/2112.09477
Publikováno v:
Journal of Artificial Intelligence Research 73 (2022) 173-208
Reinforcement learning (RL) methods usually treat reward functions as black boxes. As such, these methods must extensively interact with the environment in order to discover rewards and optimal policies. In most RL applications, however, users have t
Externí odkaz:
http://arxiv.org/abs/2010.03950
Autor:
Icarte, Rodrigo Toro, Valenzano, Richard, Klassen, Toryn Q., Christoffersen, Phillip, Farahmand, Amir-massoud, McIlraith, Sheila A.
Reinforcement Learning (RL) agents typically learn memoryless policies---policies that only consider the last observation when selecting actions. Learning memoryless policies is efficient and optimal in fully observable environments. However, some fo
Externí odkaz:
http://arxiv.org/abs/2010.01753
Autor:
Toro Icarte, Rodrigo, Klassen, Toryn Q., Valenzano, Richard, Castro, Margarita P., Waldie, Ethan, McIlraith, Sheila A.
Publikováno v:
In Artificial Intelligence October 2023 323
The pancake puzzle is a classic optimization problem that has become a standard benchmark for heuristic search algorithms. In this paper, we provide full proofs regarding the local search topology of the gap heuristic for the pancake puzzle. First, w
Externí odkaz:
http://arxiv.org/abs/1705.04665
Autor:
Valenzano, Richard
Many single-agent search algorithms have parameters that need to be tuned. Although settings found by offline tuning will exhibit strong average performance, properly selecting parameter settings for each problem can result in substantially reduced s
Externí odkaz:
http://hdl.handle.net/10048/732
Autor:
Valenzano, Richard.
Thesis (M. Sc.)--University of Alberta, 2009.
Title from PDF file main screen (viewed on Nov. 27, 2009). "A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of S
Title from PDF file main screen (viewed on Nov. 27, 2009). "A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of S
Externí odkaz:
http://hdl.handle.net/10048/732