04-02-2016, 02:46 PM
COURSE OUTLINE:
1. Introduction to AI
Principles & Developments in AI
Brief History of AI
2. Intelligent Agents (Types of)
3. Problem Solving: Search Algorithms & heuristics Uninformed Search Informed Search
4. Knowledge and Reasoning
Agents that reason logically – prepositional logic
a) Using first-order logic
b) Inference in first-order logic
5. Knowledge Representation
Rules Frames Cases Semantic Nets
6. Rule Based Systems Forward Chaining
Backward Chaining
7. Reasoning under Uncertainty
Uncertainty
Errors (types of) Uncertainty in Inference Chains
What is AI
Defn 1.
The science of making machines do things which would require intelligence if they were
done by a human. (Marvin Minsky)
Defn 2.
(a) A set of goals meant to address a (b) class of problems, using © a set of methods
by a (d) set of people
(a) A Set of Goals
• To make a machine that can do anything a human can do, i.e. – Understand natural language – Plan
– Learn from experience
The aim is to build an artifact that is intelligent like a human being
(b) A Class of Problems
• Problems that require search: – No deterministic algorithm is known. – Must use “trial and error". – Example: Schedule courses. – Non-example: Sort a class roster.
OR
• Problems that are poorly specified: – We don't know a concise, exact problem specification. – We don't know what knowledge is needed to solve the problem, and/ or we don't have
the knowledge needed to solve the problem. – Our knowledge is imprecise or inaccurate. – Example: Explain integration to a human. – Non-example: Factor an integer.
© A Set of Methods
• Use of general inference methods – such as heuristic search, constraint propagation or resolution theorem proving. • Representation of knowledge in declarative form, – such as search spaces, constraint networks
OR
A Set of Methods
• Heuristic Search
• Expert Protocols
• Iterative Programming
• More task-specific methods, e.g. for learning or planning
– Tend to cross tasks – “AI-complete” problems