1. Artificial Intelligence introduction(2)
CSE K3R20/K3R23

2. A Brief History of Artificial Intelligence
AI has roots in a number of scientific disciplines
computer science and engineering (hardware and software)
philosophy (rules of reasoning)
mathematics (logic, algorithms, optimization)
cognitive science and psychology (modeling high level human/animal thinking)
neural science (model low level human/animal brain activity)
linguistics

3. A Brief History of Artificial Intelligence
The birth of AI (1943 – 1956)
Pitts and McCulloch (1943): simplified mathematical model of neurons can realize all propositional logic primitives (can compute all Turing computable functions)
Allen Turing: Turing machine and Turing test (1950)
Claude Shannon: information theory; possibility of chess playing computers

4. A Brief History of Artificial Intelligence
Early enthusiasm (1952 – 1969)
1956 Dartmouth conference
John McCarthy (Lisp);
Marvin Minsky (first neural network machine);
Emphasize on intelligent general problem solving
Lisp (AI programming language);
Resolution by John Robinson (basis for automatic theorem proving);
heuristic search (A*, AO*, game tree search)

5. A Brief History of Artificial Intelligence
Emphasis on knowledge (1966 – 1974)
domain specific knowledge is the key to overcome existing difficulties
knowledge representation (KR) paradigms
Knowledge-based systems (1969 – 1999)
DENDRAL: the first knowledge intensive system (determining 3D structures of complex chemical compounds)
MYCIN: first rule-based expert system (containing 450 rules for diagnosing blood infectious diseases)
EMYCIN: an ES shell
PROSPECTOR: first knowledge-based system that made significant profit (geological ES for mineral deposits)

6. A Brief History of Artificial Intelligence
AI became an industry (1980 – 1989)
wide applications in various domains
commercially available tools
Current trends (1990 – present)
more realistic goals
more practical (application oriented)
distributed AI and intelligent software agents
resurgence of neural networks and emergence of genetic algorithms

7. Programming languages for AI
The relational languages like PROLOG [ PROgramming in LOgic] AND LISP [LISt Processing] in AI.
LISP is well suited for handling lists, where as PROLOG is designed for logic Programming
Architecture of AI machine
At the early stage of programs of AI, common machine used for conventional programming were also used for AI programming.
This special architecture, called LISP and PROLOG machine. Most of this architecture are used in research laboratory, and are not available in the open commercial market.

8. Possible Approaches Think Act Like humans Well Rational GPS agents
Eliza
Rational
agents
Heuristic
systems
AI tends to work mostly in this area

9. Think well
Think
Act
Like humans
Well
GPS
Eliza
Rational
agents
Heuristic
systems
Develop formal models of knowledge representation, reasoning, learning memory, problem solving, that can be rendered in algorithms.
There is often an emphasis on a systems that are provably correct, and guarantee finding an optimal solution.

10. Act well
Think
Act
Like humans
Well
GPS
Eliza
Rational
agents
Heuristic
systems
For a given set of inputs, generate an appropriate output that is not necessarily correct but gets the job done.
A heuristic (heuristic rule, heuristic method) is a rule of thumb, strategy, trick, simplification, or any other kind of device which drastically limits search for solutions in large problem spaces.
Heuristics do not guarantee optimal solutions; in fact, they do not guarantee any solution at all: all that can be said for a useful heuristic is that it offers solutions which are good enough most of the time. – Feigenbaum and Feldman, 1963, p. 6

11. Think like humans Cognitive science approach
Act
Like humans
Well
GPS
Eliza
Rational
agents
Heuristic
systems
Cognitive science approach
Focus not just on behavior and I/O but also look at reasoning process.
Computational model should reflect “how” results were obtained.
Provide a new language for expressing cognitive theories and new mechanisms for evaluating them
GPS (General Problem Solver): Goal not just to produce humanlike behavior (like ELIZA), but to produce a sequence of steps of the reasoning process that was similar to the steps followed by a person in solving the same task.

12. Act like humans Behaviorist approach.
Think
Act
Like humans
Well
GPS
Eliza
Rational
agents
Heuristic
systems
Behaviorist approach.
Not interested in how you get results, just the similarity to what human results are.
Exemplified by the Turing Test (Alan Turing, 1950).
ELIZA: A program that simulated a psychotherapist interacting with a patient and successfully passed the Turing Test.
Coded at MIT during by Joel Weizenbaum.

13. Areas of AI and their inter-dependencies
Knowledge Representation
Search
Logic
Machine Learning
Planning
Expert Systems
NLP
Vision
Robotics

14. Branches of AI Logical AI Search Natural language processing
pattern recognition
Knowledge representation
Inference From some facts, others can be inferred.
Automated reasoning
Learning from experience
Planning To generate a strategy for achieving some goal
Epistemology This is a study of the kinds of knowledge that are required for solving problems in the world.
Genetic programming
Emotions??? …

15. Thank You !!!