Search Results - (((((((kant OR agent) OR arts) OR started) OR cantor) OR anne) OR carter) OR keep) algorithms.

Beginning Java data structures and algorithms : sharpen your problem solving skills by learning core computer science concepts in a pain-free manner by Cutajar, James

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TensorFlow Reinforcement Learning Quick Start Guide : Get up and Running with Training and Deploying Intelligent, Self-Learning Agents Using Python. by Balakrishnan, Kaushik

Table of Contents: “…; Formulating the RL problem; The relationship between an agent and its environment; Defining the states of the agent; Defining the actions of the agent; Understanding policy, value, and advantage functions; Identifying episodes; Identifying reward functions and the concept of discounted rewards; Rewards; Learning the Markov decision process ; Defining the Bellman equation; On-policy versus off-policy learning…”
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Algorithms and ordering heuristics for distributed constraint satisfaction problems by Wahbi, Mohamed

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Agent-based computing

Table of Contents: “…AGENT-BASED COMPUTING -- AGENT-BASED COMPUTING -- CONTENTS -- PREFACE -- AGENT-BASED GENETIC ALGORITHM FOR GLOBAL NUMERICAL OPTIMIZATION AND FEATURE SELECTION -- 1. …”
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Multi-Agent Coordination : A Reinforcement Learning Approach. by Sadhu, Arup Kumar

Table of Contents: “…Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgments -- Chapter 1 Introduction: Multi-agent Coordination by Reinforcement Learning and Evolutionary Algorithms -- 1.1 Introduction -- 1.2 Single Agent Planning -- 1.2.1 Terminologies Used in Single Agent Planning -- 1.2.2 Single Agent Search-Based Planning Algorithms -- 1.2.2.1 Dijkstra's Algorithm -- 1.2.2.2 A* (A-star) Algorithm -- 1.2.2.3 D* (D-star) Algorithm -- 1.2.2.4 Planning by STRIPS-Like Language -- 1.2.3 Single Agent RL -- 1.2.3.1 Multiarmed Bandit Problem -- 1.2.3.2 DP and Bellman Equation…”
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Autonomous bidding agents : strategies and lessons from the trading agent competition by Wellman, Michael P.

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Security of block ciphers : from algorithm design to hardware implementation by Sakiyama, Kazuo, 1971-

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Ensemble methods : foundations and algorithms. by Zhou, Zhi-Hua (Computer scientist)

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Genome Sequencing Technology and Algorithms. by Kim, Sun

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C# Data Structures and Algorithms : Explore the possibilities of C# for developing a variety of efficient applications. by Jamro, Marcin

Table of Contents: “…InsertionRemoval; Example -- BST visualization; AVL trees; Implementation; Example -- keep the tree balanced; Red-black trees; Implementation; Example -- RBT-related features; Binary heaps; Implementation; Example -- heap sort; Binomial heaps; Fibonacci heaps; Summary; Chapter 6: Exploring Graphs; Concept of graphs; Applications; Representation; Adjacency list; Adjacency matrix; Implementation; Node; Edge; Graph; Example -- undirected and unweighted edges; Example -- directed and weighted edges; Traversal; Depth-first search; Breadth-first search; Minimum spanning tree; Kruskal's algorithm.…”
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Getting Started with Greenplum for Big Data Analytics. by Gollapudi, Sunila

Table of Contents: “…Intro -- Getting Started with Greenplum for Big Data Analytics -- Table of Contents -- Getting Started with Greenplum for Big Data Analytics -- Credits -- Foreword -- About the Author -- Acknowledgement -- About the Reviewers -- www.PacktPub.com -- Support files, eBooks, discount offers and more -- Why Subscribe? …”
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Graph algorithms and applications 3

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Algorithmics of matching under preferences by Manlove, David F.

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Evolutionary computation in gene regulatory network research

Table of Contents: “…Nobile, Davide Cipolla, Paolo Cazzaniga and Daniela Besozzi -- Modelling Dynamic Gene Expression in Streptomyces Coelicolor: Comparing Single and Multi-Objective Setups / Spencer Angus Thomas, Yaochu Jin, Emma Laing and Colin Smith -- Reconstruction of Large-Scale Gene Regulatory Network Using S-system Model / Ahsan Raja Chowdhury and Madhu Chetty -- III: EAs for Evolving GRNs and Reaction Networks -- Design Automation of Nucleic Acid Reaction System Simulated by Chemical Kinetics Based on Graph Rewriting Model / Ibuki Kawamata and Masami Hagiya -- Using Evolutionary Algorithms to Study the Evolution of Gene Regulatory Networks Controlling Biological Development / Alexander Spirov and David Holloway -- Evolving GRN-inspired In Vitro Oscillatory Systems / Quang Huy Dinh, Nathanael Aubert, Nasimul Noman, Hitoshi Iba and Yannic Rondelez -- IV: Applications of GRN with EAs -- Artificial Gene Regulatory Networks for Agent Control / Sylvain Cussat-Blanc, Jean Disset, Stéphane Sanchez and Yves Duthen -- Evolving H-GRNs for Morphogenetic Adaptive Pattern Formation of Swarm Robots / Hyondong Oh and Yaochu Jin -- Regulatory Representations in Architectural Design / Daniel Richards and Martyn Amos -- Computing with Artificial Gene Regulatory Networks / Michael A. …”
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Apache Spark Quick Start Guide : Quickly Learn the Art of Writing Efficient Big Data Applications with Apache Spark. by Mehrotra, Shrey

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Parsing schemata for practical text analysis by Gómez-Rodríguez, Carlos

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Metaheuristic optimization for the design of automatic control laws by Sandou, Guillaume

Table of Contents: “…Motivations to use metaheuristic algorithms -- 1.3. Organization of the book -- Chapter 2. …”
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Introduction to programming : learn to program in Java with data structures, algorithms, and logic by Samoylov, Nick

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Multi-agent machine learning : a reinforcement approach

Table of Contents: “…2.12 Eligibility TracesReferences; Chapter 3: Learning in Two-Player Matrix Games; 3.1 Matrix Games; 3.2 Nash Equilibria in Two-Player Matrix Games; 3.3 Linear Programming in Two-Player Zero-Sum Matrix Games; 3.4 The Learning Algorithms; 3.5 Gradient Ascent Algorithm; 3.6 WoLF-IGA Algorithm; 3.7 Policy Hill Climbing (PHC); 3.8 WoLF-PHC Algorithm; 3.9 Decentralized Learning in Matrix Games; 3.10 Learning Automata; 3.11 Linear Reward-Inaction Algorithm; 3.12 Linear Reward-Penalty Algorithm; 3.13 The Lagging Anchor Algorithm; 3.14 L R-I Lagging Anchor Algorithm; References.…”
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Foundations of decision-making agents : logic, probability and modality by Das, Subrata Kumar

Table of Contents: “…Ch. 1. Modeling agent epistemic states: an informal overview. 1.1. …”
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