What is Intelligence Foundations and Scope of AI

Lecture 1 of CS607 Artificial Intelligence introduces the core conceptual foundations and philosophical definitions of artificial intelligence. The lecture opens with the fundamental inquiry: what constitutes intelligence? While humans readily recognize intelligent behavior in living species, formulating a precise mathematical or computational definition presents deep challenges. Students explore intuitive demonstrations of intelligence, such as a mouse navigating a complex maze to find food or humans recognizing patterns in numerical sequences, establishing that problem-solving ability, adaptability, and learning are primary hallmarks of intelligence. The lecture investigates formal definitions of Artificial Intelligence grouped along two primary dimensions: thinking versus acting, and human-like performance versus ideal rationality. Students study the four classical views of AI: systems that think like humans (cognitive science), systems that act like humans (Turing test approach), systems that think rationally (laws of thought and formal logic), and systems that act rationally (rational intelligent agents). The distinction between biological intelligence and digital computation is established. The curriculum systematically bridges classical symbolic AI reasoning with modern statistical machine learning and connectionist neural networks. Reviewing the accompanying lecture handouts and conceptual diagrams reinforces understanding of knowledge bases, inference rules, and uncertain reasoning models. Consistent evaluation of state-space representations, rule activation patterns, and truth table proofs ensures robust preparation for academic assessments. Students investigate how intelligent agents perceive environmental percepts through sensors and actuate rational decisions through operational effectors. The discussion highlights how heuristic evaluation functions guide search algorithms toward goal states while dramatically reducing computational overhead. Key concepts such as chromosome encoding, fitness proportional selection, and genetic diversity preservation are analyzed in evolutionary computation. The lecture details formal logic inference mechanisms, demonstrating how resolution refutation and unification derive valid theorems from clausal knowledge bases. Production rule architectures are analyzed to understand how forward chaining and backward chaining drive automated expert diagnostic systems. CLIPS rule-based programming constructs provide practical engineering frameworks for managing working memory facts and agenda activations. Fuzzy membership functions and linguistic variables are formulated to model real-world vagueness and human approximate reasoning mathematically.