Topics in our Artificial Intelligence Notes PDF
The topics we will cover in these Artificial Intelligence Handwritten
Notes PDF will be taken from the following list:
Artificial Intelligence Introduction
Definitions: Artificial Intelligence, Intelligence, Intelligent behavior,
Understanding AI, Hard or Strong AI, Soft or Weak AI, Cognitive Science.
Goals of AI: General AI Goal, Engineering based AI Goal, Science-based
AI Goal.
AI Approaches: Cognitive science, Laws of thought, Turing Test, Rational
agent.
AI Techniques: Techniques that make the system behave as Intelligent,
Describe and match, Goal reduction, Constraint satisfaction, Tree
Searching, Generate and test, Rule-based systems.
Biology-inspired AI Techniques: Neural Networks, Genetic Algorithms,
Reinforcement learning.
Branches of AI: Logical AI, Search in AI, Pattern Recognition, Knowledge
Representation, Inference, Commonsense knowledge and reasoning,
Learning, Planning, Epistemology, Ontology, Heuristics, Genetic
programming.
Applications of AI: Game playing, Speech Recognition, Understanding
Natural Language, Computer Vision, Expert Systems.
Problem Solving, Search Strategies
General Problem Solving Problem solving definitions: problem space,
problem-solving, state space, state change, the structure of state space,
problem solution, problem description; Examples of problem definition.
Search and Control Strategies Search related terms: algorithm’s
performance and complexity, computational complexity, “Big – O”
notations, tree structure, stacks, and queues; Search: search algorithms,
hierarchical representation, search space, the formal statement, search
notations, estimate cost, and heuristic function; Control strategies:
strategies for search, forward and backward chaining.
Exhaustive Searches Depth-first search Algorithm; Breadth-first search
Algorithm; Compare depth-first and breadth-first search;
Heuristic Search Techniques Characteristics of heuristic search;
Heuristic search compared with another search; Example of heuristic
search; Types of heuristic search algorithms
Constraint Satisfaction Problems (CSPs) and Models Examples of
CSPs; Constraint Satisfaction Models: Generate and Test, Backtracking
algorithm, Constraint Satisfaction Problems (CSPs): definition, properties,
and algorithms.
, Knowledge Representation
Knowledge Representation Introduction – Knowledge Progression, KR
model, category: typology map, type, relationship, framework, mapping,
forward & backward representation, KR system requirements; KR
schemes – relational, inheritable, inferential, declarative, procedural; KR
issues – attributes, relationship, granularity.
KR Using Predicate Logic Logic as language; Logic representation:
Propositional logic, statements, variables, symbols, connective, truth
value, contingencies, tautologies, contradictions, antecedent, consequent,
argument; Predicate logic – predicate, logic expressions, quantifiers,
formula; Representing “IsA” and “Instance” relationships; Computable
functions and predicates; Resolution.
KR Using Rules Types of Rules – declarative, procedural, meta-rules;
Procedural versus declarative knowledge & language; Logic programming
– characteristics, statement, language, syntax & terminology, Data
components – simple & structured data objects, Program Components –
clause, predicate, sentence, subject, queries; Programming paradigms –
models of computation, imperative model, functional model, logic model;
Reasoning – Forward and backward chaining, conflict resolution; Control
knowledge.
Reasoning System
Reasoning: Definitions Reasoning, formal logic, and informal logic,
uncertainty, monotonic logic, non-monotonic Logic; Methods of reasoning
and examples – deductive, inductive, abductive, analogy; Sources of
uncertainty; Reasoning and KR; Approaches to reasoning – symbolic,
statistical, and fuzzy.
Symbolic Reasoning: Non-monotonic reasoning – Default Reasoning,
Circumscription, Truth Maintenance Systems; Implementation issues.
Statistical Reasoning: Glossary of terms; Probability and Bayes’
theorem – probability, Bayes’ theorem, examples; Certainty factors rule-
based systems; Bayesian networks and certainty factors – Bayesian
networks; Dempster Shafer theory – model, belief and plausibility,
calculus, combining beliefs; Fuzzy logic – description, membership.
Game Theory
Overview Definition of Game, Game theory, Relevance of Game theory
and Game plying, Glossary of terms – Game, Player, Strategy, Zero-Sum
game, Constant-Sum game, Nonzero-Sum game, Prisoner’s dilemma, N-
Person Game, Utility function, Mixed strategies, Expected payoff, Mini-Max
theorem, Saddle point; Taxonomy of games.
The topics we will cover in these Artificial Intelligence Handwritten
Notes PDF will be taken from the following list:
Artificial Intelligence Introduction
Definitions: Artificial Intelligence, Intelligence, Intelligent behavior,
Understanding AI, Hard or Strong AI, Soft or Weak AI, Cognitive Science.
Goals of AI: General AI Goal, Engineering based AI Goal, Science-based
AI Goal.
AI Approaches: Cognitive science, Laws of thought, Turing Test, Rational
agent.
AI Techniques: Techniques that make the system behave as Intelligent,
Describe and match, Goal reduction, Constraint satisfaction, Tree
Searching, Generate and test, Rule-based systems.
Biology-inspired AI Techniques: Neural Networks, Genetic Algorithms,
Reinforcement learning.
Branches of AI: Logical AI, Search in AI, Pattern Recognition, Knowledge
Representation, Inference, Commonsense knowledge and reasoning,
Learning, Planning, Epistemology, Ontology, Heuristics, Genetic
programming.
Applications of AI: Game playing, Speech Recognition, Understanding
Natural Language, Computer Vision, Expert Systems.
Problem Solving, Search Strategies
General Problem Solving Problem solving definitions: problem space,
problem-solving, state space, state change, the structure of state space,
problem solution, problem description; Examples of problem definition.
Search and Control Strategies Search related terms: algorithm’s
performance and complexity, computational complexity, “Big – O”
notations, tree structure, stacks, and queues; Search: search algorithms,
hierarchical representation, search space, the formal statement, search
notations, estimate cost, and heuristic function; Control strategies:
strategies for search, forward and backward chaining.
Exhaustive Searches Depth-first search Algorithm; Breadth-first search
Algorithm; Compare depth-first and breadth-first search;
Heuristic Search Techniques Characteristics of heuristic search;
Heuristic search compared with another search; Example of heuristic
search; Types of heuristic search algorithms
Constraint Satisfaction Problems (CSPs) and Models Examples of
CSPs; Constraint Satisfaction Models: Generate and Test, Backtracking
algorithm, Constraint Satisfaction Problems (CSPs): definition, properties,
and algorithms.
, Knowledge Representation
Knowledge Representation Introduction – Knowledge Progression, KR
model, category: typology map, type, relationship, framework, mapping,
forward & backward representation, KR system requirements; KR
schemes – relational, inheritable, inferential, declarative, procedural; KR
issues – attributes, relationship, granularity.
KR Using Predicate Logic Logic as language; Logic representation:
Propositional logic, statements, variables, symbols, connective, truth
value, contingencies, tautologies, contradictions, antecedent, consequent,
argument; Predicate logic – predicate, logic expressions, quantifiers,
formula; Representing “IsA” and “Instance” relationships; Computable
functions and predicates; Resolution.
KR Using Rules Types of Rules – declarative, procedural, meta-rules;
Procedural versus declarative knowledge & language; Logic programming
– characteristics, statement, language, syntax & terminology, Data
components – simple & structured data objects, Program Components –
clause, predicate, sentence, subject, queries; Programming paradigms –
models of computation, imperative model, functional model, logic model;
Reasoning – Forward and backward chaining, conflict resolution; Control
knowledge.
Reasoning System
Reasoning: Definitions Reasoning, formal logic, and informal logic,
uncertainty, monotonic logic, non-monotonic Logic; Methods of reasoning
and examples – deductive, inductive, abductive, analogy; Sources of
uncertainty; Reasoning and KR; Approaches to reasoning – symbolic,
statistical, and fuzzy.
Symbolic Reasoning: Non-monotonic reasoning – Default Reasoning,
Circumscription, Truth Maintenance Systems; Implementation issues.
Statistical Reasoning: Glossary of terms; Probability and Bayes’
theorem – probability, Bayes’ theorem, examples; Certainty factors rule-
based systems; Bayesian networks and certainty factors – Bayesian
networks; Dempster Shafer theory – model, belief and plausibility,
calculus, combining beliefs; Fuzzy logic – description, membership.
Game Theory
Overview Definition of Game, Game theory, Relevance of Game theory
and Game plying, Glossary of terms – Game, Player, Strategy, Zero-Sum
game, Constant-Sum game, Nonzero-Sum game, Prisoner’s dilemma, N-
Person Game, Utility function, Mixed strategies, Expected payoff, Mini-Max
theorem, Saddle point; Taxonomy of games.
