Reference number : GC-2017-Q1-02550, amendment number : , agreement type : G, recipient legal name : Buro, Michael (University of Alberta), recipient country : CA, recipient province : nan, recipient city : Alberta, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : Artificial Intelligence (AI) research has come a long way creating systems that challenge human supremacy in complex decision domains such as Chess, Poker, Jeopardy, stock trading, and recently image recognition, autonomous driving, and the Asian boardgame Go. By contrast, AI progress in popular video games (such as StarCraft) and complex imperfect information domains, in which information is hidden from agents, has been slow, and in many cases human experts can still easily outperform the best programs. 
The human advantage in these domains can in part be attributed to our abilities to simplify problems while maintaining solutions, to search at different abstraction levels (e.g., looking into details only when high-level solution concepts don't seem to work), to adjust behaviour to opponents and partners, and to infer intentions from observed actions. 
Machines achieving human proficiency in such areas will have a huge impact on technology, ranging from creating effective multi-robot coordination in construction tasks and rescue missions to automating naval battle groups, which can greatly benefit the Canadian economy and security. 
In this proposal I will lay out a research plan aiming at closing human-machine performance gaps in decision domains with large structured action sets, in which classical search algorithms fail due to combinatorial explosion (Theme 1), and in domains featuring agent cooperation and uncertainty created by hiding information from agents (Theme 2). 
The focus of the proposed work is to explore how to better integrate heuristic search (which can evaluate the merit of actions by looking ahead) with machine learning to deal with large combinatorial action spaces, imperfect information, and agent cooperation. In these areas the gained general insights will be used to develop AI systems able to compete with human experts in challenging decision domains, which include modern video games, military combat simulations, and traffic light optimization for Theme 1, and team games, such as Contract Bridge, and combat support systems with human operators in the loop for Theme 2. 
There are numerous application areas that can benefit from the proposed work. Video game companies, for instance, can create AI systems that improve gaming experiences by adjusting to players based on analyzing game records, or by using stronger AI systems to fine-tune parameters via self-play. Likewise, learning to orchestrate dozens of acting entities in real-time settings can improve traffic flow, military training simulators, as well as distributed weapon systems, and being able to learn to cooperate with humans will also be important in the future, with robots becoming ever more autonomous., program purpose french : Artificial Intelligence (AI) research has come a long way creating systems that challenge human supremacy in complex decision domains such as Chess, Poker, Jeopardy, stock trading, and recently image recognition, autonomous driving, and the Asian boardgame Go. By contrast, AI progress in popular video games (such as StarCraft) and complex imperfect information domains, in which information is hidden from agents, has been slow, and in many cases human experts can still easily outperform the best programs. 
The human advantage in these domains can in part be attributed to our abilities to simplify problems while maintaining solutions, to search at different abstraction levels (e.g., looking into details only when high-level solution concepts don't seem to work), to adjust behaviour to opponents and partners, and to infer intentions from observed actions. 
Machines achieving human proficiency in such areas will have a huge impact on technology, ranging from creating effective multi-robot coordination in construction tasks and rescue missions to automating naval battle groups, which can greatly benefit the Canadian economy and security. 
In this proposal I will lay out a research plan aiming at closing human-machine performance gaps in decision domains with large structured action sets, in which classical search algorithms fail due to combinatorial explosion (Theme 1), and in domains featuring agent cooperation and uncertainty created by hiding information from agents (Theme 2). 
The focus of the proposed work is to explore how to better integrate heuristic search (which can evaluate the merit of actions by looking ahead) with machine learning to deal with large combinatorial action spaces, imperfect information, and agent cooperation. In these areas the gained general insights will be used to develop AI systems able to compete with human experts in challenging decision domains, which include modern video games, military combat simulations, and traffic light optimization for Theme 1, and team games, such as Contract Bridge, and combat support systems with human operators in the loop for Theme 2. 
There are numerous application areas that can benefit from the proposed work. Video game companies, for instance, can create AI systems that improve gaming experiences by adjusting to players based on analyzing game records, or by using stronger AI systems to fine-tune parameters via self-play. Likewise, learning to orchestrate dozens of acting entities in real-time settings can improve traffic flow, military training simulators, as well as distributed weapon systems, and being able to learn to cooperate with humans will also be important in the future, with robots becoming ever more autonomous., agreement title english : Learning and Search in Decision Domains Featuring Large Action Sets and Uncertainty, agreement title french : Learning and Search in Decision Domains Featuring Large Action Sets and Uncertainty, agreement number : RGPIN, agreement value : 20000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2018-2019), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2018-2019), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada