Multilayer Perceptron and Backpropagation Algorithm

Lecture 39 of CS607 Artificial Intelligence explores how Multilayer Perceptrons (MLP) overcome the linear separability limitations of single perceptrons by introducing one or more Hidden Layers between inputs and outputs. By combining multiple non-linear hyperplanes, an MLP can construct arbitrary convex and non-convex decision boundaries, solving the XOR problem and complex classification tasks with ease. The lecture introduces smooth, differentiable continuous activation functions—such as the Sigmoid function sigma(z) = 1 / (1 + e^-z)—which are essential for gradient calculus. Students master the Backpropagation Algorithm, popularized by Rumelhart, Hinton, and Williams: forward pass computes activations; error E = 0.5 * sum((target - output)^2) is calculated at the output layer; and the chain rule of differential calculus propagates error deltas backward through hidden layers, updating weights in the opposite direction of the error gradient (gradient descent). Mastering these foundational intelligence frameworks empowers computer science scholars to design autonomous agents capable of complex decision-making in dynamic environments. Analytical exercises, search trees, and solved numerical problems help students prepare thoroughly for Virtual University midterm and final term examinations. 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.