1 / 26

Artificial Intelligence Techniques

Introduction to Artificial Intelligence. Artificial Intelligence Techniques. Artificial Intelligence. AI is often divided into two basic ‘camps’ Rule-based systems (RBS) Biological inspired, such as Artificial neural networks (ANN) There are also search methods which some people include.

ptobias
Download Presentation

Artificial Intelligence Techniques

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Introduction to Artificial Intelligence Artificial Intelligence Techniques

  2. Artificial Intelligence • AI is often divided into two basic ‘camps’ • Rule-based systems (RBS) • Biological inspired, such as Artificial neural networks (ANN) • There are also search methods which some people include. • Increasingly hybridisation.

  3. In the module • Evolutionary algorithm • Neural networks • Fuzzy Logic • Expert Systems and Knowledge Processing • Searching • Internet and AI

  4. Examples • Focus of the applications is the early part of the module is on: • Games • Robotics • Engineering and medicine

  5. Assessment • Two assignments • mini-projects • Applying AI to tasks • Early part in Java

  6. Example Areas

  7. Multi-layered perceptron (Taken from Picton 2004) Output layer Input layer Hidden layer

  8. mutation recombination The Ingredients( Taken from: EvoNet Flying Circus www2.cs.uh.edu/~ceick/ai/EC1.ppt ) t + 1 t reproduction selection

  9. Depth-First SearchTaken from Jones (2005)

  10. Breadth-First SearchTaken from Jones (2005)

  11. Knowledge Processing • Introduce types of reasoning • Deterministic • Propositional logic • Predicate logic • Dynamic-non-monotonic • Non-deterministic

  12. Using an Expert System • Taken from Johnson and Picton (1995)

  13. Internet and ‘AI’ • Week A – AI on internet, basic introduction to semantic web, agents. • Week B – Microformats • Week C – Collective Intelligence and searching 1 • Week D – Collective Intelligence and searching 2

  14. Basic structures

  15. Data Structures-Linked List

  16. Data Structures - Stack

  17. Data Structures - Queue

  18. Summary • Introduced the module • Introduced different types of AI • Structures

  19. Task 1: Finite-state machines

  20. Outcomes • By the end of the session you should: • Understand what a state diagram is. • Understand the principles of a finite state machine • Describe a simple system using a state diagram • Applications using state diagrams

  21. What is a state?

  22. State diagram (Taken from Picton 2004) yes State 1 wait for a cup to be placed Cup? State 0 wait for the button to be pressed yes yes End? no Button?

  23. Next-state table (Taken from Picton 2004)

  24. Where are they used? • Designing systems • Games

  25. Your designing a character for a maze-based game. • You must design a state diagram and table for the character.

  26. Further reading and references • http://en.wikipedia.org/wiki/Finite_state_machine • Picton PD (2004) CSY3011 Artificial Neural Networks, University College Northampton

More Related