Reference number : GC-2017-Q1-02261, amendment number : , agreement type : G, recipient legal name : Denzinger, Jörg (University of Calgary), 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 : The long-term vision for my research is to use machine learning to improve the development of distributed systems software. My key idea is to modify and extend a concept for learning of cooperative behaviour to include the results of a learning-based testing component that finds negative consequences of the learned cooperative behaviour, including problems with intended functionality, efficiency and security. 
To learn cooperative behaviour, we see the components of a distributed system as agents that can perform actions. The learning tries to create controls for these agents, so that they together achieve the functionality the whole group of agents is tasked with. 
Using learning-based testing for finding negative consequences is an evolutionary learning process in which the testing component creates interaction sequences with the tested system and evaluates the sequences based on how near they come to a testing goal. The evaluation is used to learn sequencers that come nearer and nearer to creating the negative consequences the testing system is looking for. 
The main research challenge is how to combine the focus of learning of cooperative behaviour on trying to deal with all possible situations the system can be in with the focus of learning-based testing on finding one particular sequence of situations revealing a particular problem of the tested system. Possible solutions of this challenge can target the way learning-based testing is performed, generalizing its results, but also modifying the general learner and the architecture with exception rules. In earlier work, we have developed the shout-ahead architecture for agents and a hybrid learning method for this architecture, combining reinforcement learning with evolutionary learning. A proof-of-concept system for developing control AIs for enemy units for the game Battle for Wesnoth showed that the best learned AIs are able to beat the rather good human-created central AI that comes with the game. We explored the usage of learning of behavior to test systems for weaknesses for several applications, including games. In all these applications, our test systems were able to find several weaknesses in the tested systems, including efficiency and security weaknesses. 
Building on these works, we will explore our ideas for combining general learning of behaviour with learning-based testing in two case studies, an extension of the system for Battle for Wesnoth and an automated cattle distribution center as an example of Internet of Things applications. The anticipated result of this research is the ability to create customized open distributed systems at lower cost than current practise and with higher quality due to automization., program purpose french : The long-term vision for my research is to use machine learning to improve the development of distributed systems software. My key idea is to modify and extend a concept for learning of cooperative behaviour to include the results of a learning-based testing component that finds negative consequences of the learned cooperative behaviour, including problems with intended functionality, efficiency and security. 
To learn cooperative behaviour, we see the components of a distributed system as agents that can perform actions. The learning tries to create controls for these agents, so that they together achieve the functionality the whole group of agents is tasked with. 
Using learning-based testing for finding negative consequences is an evolutionary learning process in which the testing component creates interaction sequences with the tested system and evaluates the sequences based on how near they come to a testing goal. The evaluation is used to learn sequencers that come nearer and nearer to creating the negative consequences the testing system is looking for. 
The main research challenge is how to combine the focus of learning of cooperative behaviour on trying to deal with all possible situations the system can be in with the focus of learning-based testing on finding one particular sequence of situations revealing a particular problem of the tested system. Possible solutions of this challenge can target the way learning-based testing is performed, generalizing its results, but also modifying the general learner and the architecture with exception rules. In earlier work, we have developed the shout-ahead architecture for agents and a hybrid learning method for this architecture, combining reinforcement learning with evolutionary learning. A proof-of-concept system for developing control AIs for enemy units for the game Battle for Wesnoth showed that the best learned AIs are able to beat the rather good human-created central AI that comes with the game. We explored the usage of learning of behavior to test systems for weaknesses for several applications, including games. In all these applications, our test systems were able to find several weaknesses in the tested systems, including efficiency and security weaknesses. 
Building on these works, we will explore our ideas for combining general learning of behaviour with learning-based testing in two case studies, an extension of the system for Battle for Wesnoth and an automated cattle distribution center as an example of Internet of Things applications. The anticipated result of this research is the ability to create customized open distributed systems at lower cost than current practise and with higher quality due to automization., agreement title english : Improved learning of reactive, cooperative behaviour through learning-based testing, agreement title french : Improved learning of reactive, cooperative behaviour through learning-based testing, agreement number : RGPIN, agreement value : 100000.0, 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 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), 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