![]() ![]() 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.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.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.Heuristic Search Techniques Characteristics of heuristic search Heuristic search compared with another search Example of heuristic search Types of heuristic search algorithms.Exhaustive Searches Depth-first search Algorithm Breadth-first search Algorithm Compare depth-first and breadth-first search.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.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.Applications of AI Game playing, Speech Recognition, Understanding Natural Language, Computer Vision, Expert Systems.Branches of AI Logical AI, Search in AI, Pattern Recognition, Knowledge Representation, Inference, Commonsense knowledge and reasoning, Learning, Planning, Epistemology, Ontology, Heuristics, Genetic programming.AI Techniques Techniques that make system to 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.AI Approaches Cognitive science, Laws of thought, Turing Test, Rational agent.Goals of AI General AI Goal, Engineering based AI Goal, Science-based AI Goal. ![]() Definitions Artificial Intelligence, Intelligence, Intelligent behaviour, Understanding AI, Hard or Strong AI, Soft or Weak AI, Cognitive Science. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |