Exercises for Artificial Intelligence: Exercises 2 – Agent Behaviour1 1st edition by William John Teahan

artificial-intelligence-ex2The list of exercises, chapter headings and section, and NetLogo models in this book closely follow what is in the companion “Artificial Intelligence – Agent Behaviour I ” book. The best way to learn about what is written in the companion book is to try out each of the NetLogo models that are described in the book and in the exercises in this book. A table listing all the models described in this book and the companion book is also provided. Each entry in the table lists the name of the model, the exercises where it is described, a short description of the model, and a URL where it can be found. Each of these models have sections in the Information tab that provide various documentation, such as: what the model is; how it works; how to use it; the meaning of each of the Interface’s buttons, sliders, switches, choosers, monitors, plots and output; important things to notice; things to try out; suggestions for extending the model; explanations of interesting NetLogo features used in the model; credits and references; and links to related models. In particular, the sections on how to use it, things to notice and things to try out provide some suggestions on various things a user can try when playing with the models.


In this book the readers will read What is behavior? Reactive versus Cognitive Agents, Emergence, Self-organization, Adaptivity and Evolution, The frame of reference problem, Stigmergy and Swarm Intelligence, Implementing behavior of Turtle Agents in NetLogo, Communication, Information and Language, The diversity of human language, Communication via communities of agents, Communicating Behavior, The small world phenomenon and Dijkstra’s algorithm, Using communicating agents for searching networks, Entropy and Information, Calculating Entropy in NetLogo, Language Modeling, Entropy of a Language, Communicating Meaning, Implementing uninformed search in NetLogo, Search as behavior selection, Informed search, Local search and optimization, Comparing the search behaviors, Knowledge and Knowledge-based systems, Knowledge as justified true belief, Different types of knowledge, Some approaches to Knowledge Representation and AI, Knowledge engineering problems, Knowledge without representation, Representing knowledge using maps, Representing knowledge using event maps, Representing knowledge using rules and logic, Reasoning using rules and logic, Knowledge and reasoning using frames, Knowledge and reasoning using decision trees, The nature of intelligence, Intelligence without representation and reason, What AI can and can’t do, The need for design objectives for Artificial Intelligence, What are good objectives? Some Design Objectives for Artificial Intelligence, Towards believable agents, Towards computers with problem solving ability and much more.

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 Click here to download Artificial Intelligence: Exercises 2 – Agent Behaviour I 1st edition

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