Elicitation and Planning in Markov Decision Processes with Unknown Rewards - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Elicitation and Planning in Markov Decision Processes with Unknown Rewards write by Pegah Alizadeh. This book was released on 2016. Elicitation and Planning in Markov Decision Processes with Unknown Rewards available in PDF, EPUB and Kindle. Markov decision processes (MDPs) are models for solving sequential decision problemswhere a user interacts with the environment and adapts her policy by taking numericalreward signals into account. The solution of an MDP reduces to formulate the userbehavior in the environment with a policy function that specifies which action to choose ineach situation. In many real world decision problems, the users have various preferences,and therefore, the gain of actions on states are different and should be re-decoded foreach user. In this dissertation, we are interested in solving MDPs for users with differentpreferences.We use a model named Vector-valued MDP (VMDP) with vector rewards. We propose apropagation-search algorithm that allows to assign a vector-value function to each policyand identify each user with a preference vector on the existing set of preferences wherethe preference vector satisfies the user priorities. Since the user preference vector is notknown we present several methods for solving VMDPs while approximating the user'spreference vector.We introduce two algorithms that reduce the number of queries needed to find the optimalpolicy of a user: 1) A propagation-search algorithm, where we propagate a setof possible optimal policies for the given MDP without knowing the user's preferences.2) An interactive value iteration algorithm (IVI) on VMDPs, namely Advantage-basedValue Iteration (ABVI) algorithm that uses clustering and regrouping advantages. Wealso demonstrate how ABVI algorithm works properly for two different types of users:confident and uncertain.We finally work on a minimax regret approximation method as a method for findingthe optimal policy w.r.t the limited information about user's preferences. All possibleobjectives in the system are just bounded between two higher and lower bounds while thesystem is not aware of user's preferences among them. We propose an heuristic minimaxregret approximation method for solving MDPs with unknown rewards that is faster andless complex than the existing methods in the literature.
Regret-based Reward Elicitation for Markov Decision Processes
Regret-based Reward Elicitation for Markov Decision Processes - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Regret-based Reward Elicitation for Markov Decision Processes write by Regan Kevin. This book was released on 2014. Regret-based Reward Elicitation for Markov Decision Processes available in PDF, EPUB and Kindle.
Cognitive Electronic Warfare: An Artificial Intelligence Approach
Cognitive Electronic Warfare: An Artificial Intelligence Approach - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Cognitive Electronic Warfare: An Artificial Intelligence Approach write by Karen Haigh. This book was released on 2021-07-31. Cognitive Electronic Warfare: An Artificial Intelligence Approach available in PDF, EPUB and Kindle. This comprehensive book gives an overview of how cognitive systems and artificial intelligence (AI) can be used in electronic warfare (EW). Readers will learn how EW systems respond more quickly and effectively to battlefield conditions where sophisticated radars and spectrum congestion put a high priority on EW systems that can characterize and classify novel waveforms, discern intent, and devise and test countermeasures. Specific techniques are covered for optimizing a cognitive EW system as well as evaluating its ability to learn new information in real time. The book presents AI for electronic support (ES), including characterization, classification, patterns of life, and intent recognition. Optimization techniques, including temporal tradeoffs and distributed optimization challenges are also discussed. The issues concerning real-time in-mission machine learning and suggests some approaches to address this important challenge are presented and described. The book covers electronic battle management, data management, and knowledge sharing. Evaluation approaches, including how to show that a machine learning system can learn how to handle novel environments, are also discussed. Written by experts with first-hand experience in AI-based EW, this is the first book on in-mission real-time learning and optimization.
Algorithmic Decision Theory
Algorithmic Decision Theory - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Algorithmic Decision Theory write by Toby Walsh. This book was released on 2015-08-27. Algorithmic Decision Theory available in PDF, EPUB and Kindle. This book constitutes the thoroughly refereed conference proceedings of the 4th International Conference on Algorithmic Decision Theory , ADT 2015, held in September 2015 in Lexington, USA. The 32 full papers presented were carefully selected from 76 submissions. The papers are organized in topical sections such as preferences; manipulation, learning and other issues; utility and decision theory; argumentation; bribery and control; social choice; allocation and other problems; doctoral consortium.
Markov Decision Processes in Artificial Intelligence
Markov Decision Processes in Artificial Intelligence - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Markov Decision Processes in Artificial Intelligence write by Olivier Sigaud. This book was released on 2013-03-04. Markov Decision Processes in Artificial Intelligence available in PDF, EPUB and Kindle. Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision problems under uncertainty as well as reinforcement learning problems. Written by experts in the field, this book provides a global view of current research using MDPs in artificial intelligence. It starts with an introductory presentation of the fundamental aspects of MDPs (planning in MDPs, reinforcement learning, partially observable MDPs, Markov games and the use of non-classical criteria). It then presents more advanced research trends in the field and gives some concrete examples using illustrative real life applications.
