Psychology of Social Isolation

Study Notes

Course Information

Bharati Vidyapeeth, Pune
Deemed to be University
Accredited with 'A+' Grade (2017) by NAAC
Category I University Status by UGC
A Grade University Status by MHRD, Govt. of India
School of Distance Education
MCA Sem III (301)
Artificial Intelligence

Unit 1: Introduction

Objective: Understand the basics of Artificial Intelligence, its types, and applications.
What is AI?: A technique to develop software programs that enable computers to perform tasks requiring human intelligence.
Background of AI: The history of AI, starting with early philosophers attempting to simulate human thought processes.
Types of AI Problems: Various areas where AI is applied, including intelligent game playing, natural language processing, vision processing, and speech processing.

Unit 2: Introduction and Historical Perspective, Hard and Soft AI

Objective: Study historical perspective of AI and various search techniques.
Introduction to disciplines and applications of AI, focusing on the aspects of computer science, mathematics, psychology, cognition, biology, neurology, and philosophy.
AI as combination of disciplines: The different fields that contribute to the development and application of AI.
AI theories of intelligence: Including knowledge-based and other approaches.
Knowledge-based approach: An approach to artificial intelligence that aims to capture the knowledge of human experts for decision-making, using a knowledge base and an inference engine to solve problems.
Rule-based systems: A knowledge representation technique that uses rules to represent knowledge in a knowledge-based system.
Logic programming: A knowledge representation technique that uses logic to determine the truth or falsehood of a proposition. Prolog is a common example of logic programming.
Symbolic computation: The manipulation of symbols rather than numbers to solve problems in AI, crucial for tasks like knowledge representation and inference.
Decision support analysis: Building on knowledge bases, these systems involve generating alternatives and making selections.
Criticism of symbolic AI: This approach, focused on symbolic manipulation, has some limitations when dealing with incomplete information or real-world complexity.
Types of knowledge-based systems: Expert Systems, Rule-Based Systems
State space search and heuristic search techniques: Different methods for finding solutions in complex problems, involving exploring various possible states and making decisions.

Unit 3: Knowledge Representation Issues

Objective: Understand various approaches to knowledge representation.
Introduction: Discussing the concept of knowledge representation for AI problems.
Knowledge representation as facts: How to represent knowledge in a way that a computer system can use.
Knowledge representation using propositional logic: Describe knowledge representation with logical propositions. Includes details and example about logical inferences.
First-order logic (FOL): Introduce FOL which provides a powerful formal language for representing knowledge, including quantified statements over objects, relations, and functions.

Unit 4: Symbolic Reasoning Under Uncertainty

Objective: Become familiar with symbolic reasoning techniques under conditions of uncertainty.
Introduction to reasoning: The process of deriving conclusions from premises in AI.
Non-monotonic reasoning: Knowledge bases where new information can invalidate previously accepted conclusions; typical in real-world tasks.
Statistical Reasoning/Probability Reasoning: A brief introduction to probability reasoning such as Bayes’ Theorem and applications.
Fuzzy Logic: A knowledge representation technique dealing with uncertain or imprecise information, often used in expert systems.