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Artificial Intelligence: Exercises II

Agent Behaviour I

Artificial Intelligence: Exercises II
3,8 (12 Bewertungen)
ISBN: 978-87-7681-592-9
1. Auflage
Seiten : 182
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This “Artificial Intelligence: Exercises II” eBook will guide you through useful exercises related to the “Agent Behavior” text. Both the source text and this exercise eBook can be downloaded for free

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Über das Buch

  1. Beschreibung
  2. Inhalt
  3. Über den Autor


Artificial Intelligence, or AI, is the study and development of computer systems which can perform tasks which normally require human intelligence. Agent Behaviour is a complex aspect of AI, but integral to one’s understanding of the subject. It is discussed in detail in William John Teahan’s “Artificial Intelligence - Agent Behaviour I” e-book.

This “Artificial Intelligence: Exercises II” e-book will guide you through useful exercises related to the “Agent Behavior” text. Both the source text and this exercise e-book can be downloaded for free.

Exercises within this AI workbook help the reader understand a wide range of topics, including the difference between reactive and cognitive agents, stigmergy, swarm intelligence, Dijkstra’s algorithm, knowledge-based systems, the limitations of AI, and advancements towards computers with problem solving abilities. Each of the exercises correspond to a NetLogo model which can be accessed online and manipulated for learning purposes. Solutions to selected exercises follow the text.

Readers interested in this subject matter should also refer to the “Artificial Intelligence: Exercises I” e-workbook which is also available on



6. Behaviour
6.1 What is behaviour?
6.2 Reactive versus Cognitive Agents
6.3 Emergence, Self-organisation, Adaptivity and Evolution
6.4 The Frame of Reference Problem
6.5 Stigmergy and Swarm Intelligence
6.6 Implementing behaviour of Turtle Agents in NetLogo
6.7 Boids

7. Communication
7.1 Communication, Information and Language
7.2 The diversity of human language
7.3 Communication via communities of agents
7.4 Communicating Behaviour
7.5 The Small World Phenomenon and Dijkstra’s algorithm
7.6 Using communicating agents for searching networks
7.7 Entropy and Information
7.8 Calculating Entropy in NetLogo
7.9 Language Modelling
7.10 Entropy of a Language
7.11 Communicating Meaning

8. Search
8.1 Search Behaviour
8.2 Search Problems
8.3 Uninformed (blind) search
8.4 Implementing uninformed search in NetLogo
8.5 Search as behaviour selection
8.6 Informed search
8.7 Local search and optimisation
8.8 Comparing the search behaviours

9. Knowledge
9.1 Knowledge and Knowledge-based Systems
9.2 Knowledge as justified true belief
9.3 Different types of knowledge
9.4 Some approaches to Knowledge Representation and AI
9.5 Knowledge engineering problems
9.6 Knowledge without representation
9.7 Representing knowledge using maps
9.8 Representing knowledge using event maps
9.9 Representing knowledge using rules and logic
9.10 Reasoning using rules and logic
9.11 Knowledge and reasoning using frames
9.12 Knowledge and reasoning using decision trees
9.13 Knowledge and reasoning using semantic networks

10. Intelligence
10.1 The nature of intelligence
10.2 Intelligence without representation and reason
10.3 What AI can and can’t do
10.4 The Need for Design Objectives for Artificial Intelligence
10.5 What are Good Objectives?
10.6 Some Design Objectives for Artificial Intelligence
10.7 Towards believable agents
10.8 Towards computers with problem solving ability

Solutions to Selected Exercises

Über den Autor

Dr Teahan is a Lecturer in the School of Computer Science at Bangor University. His research focusses on Artificial Intelligence, Intelligent Agents and Information Extraction. His research has specifically focused on applying text compression-based language models to Information Retrieval (IR), Natural Language Porcessing and Information Extraction. Before coming to Bangor, he was a research fellow with the Information Retrieval Group under Prof. David Harper at The Robert Gordon University in Aberdeen, Scotland from 1999-2000; an invited researcher in the Information Theory Dept. at Lund University in Sweden in 1999; and a Research Assistant in the Machine Learning and Digital Libraries Labs at the University of Waikato in New Zealand in 1998. At Waikato, he completed his Ph.D. in 1998 on applying text compression models to the problem of modelling English text.

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