Introduction to Soft Computing ECE457 Applied Artificial Intelligence Spring 2007 Lecture #12 ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 2 Outline Overview of soft computing Neural networks Russell & Norvig, sections 20.5 Fuzzy logic Russell & Norvig, pages Genetic algorithms Russell & Norvig, pages ECE 493 & ECE 750 (Prof. Karray) ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 3 Soft Computing Branch of AI that deals with systems and methodologies that can perform approximate, qualitative, human-like reasoning Humans can make intelligent decisions using incomplete and imprecise information Soft reasoning Computer algorithms require complete and precise information Hard reasoning Soft computing aims to bridge this gap ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 4 Soft vs. Hard Computing Hard ComputingSoft Computing Input Complete and exact data Approximate and incomplete data Output Overly-exact solution Solution thats good enough ReasoningRationalHuman-like ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 5 Soft Computing Probabilistic reasoning Human randomness Artificial neural networks (ANN) Human brain Fuzzy logic (FL) Human knowledge Evolutionary computing Genetic algorithms (GA) Biological evolution ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 6 Artificial Neural Networks Human Brain Massively parallel network of neurons Each individual neuron is not intelligent Neuron is a simple computing element But the brain is intelligent! ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 7 Human Brain Brain learns by changing and adjusting the connections between neurons Information encoded in many ways Connection patterns of neurons Amplification of signals by dendrites Transfer function and threshold values controlling whether the cell transmits the signal ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 8 Neurons Receives electric impulse from other neurons through dendrites. If impulse strong enough, travels through axon. Reaches synapses and transmits to other neurons ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 9 Artificial Neuron i th neuron sums inputs a 1 a j a n, weighted by weights w i1 w ij w in Threshold value i controls activation If activated, input is transformed by transfer function f i (.) into output a i a i = f i ( j w ij a j - i ) = [0, 1] ajaj w ij ii aiai f i (.) Dendrites Axon Synapse Cell body ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 10 Artificial Neural Networks Large arrangement of inter-connected artificial neurons Different classes of network Topology of the network Transfer function of the neurons Learning algorithm Different networks appropriate for different applications ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 11 Perceptron Simplest and most commonly-used ANN Feed-forward ANN Single-layer No hidden layer Only linearly- separable problems Multi-layer Non-linear problems x1x1 x2x2 x3x3 o1o1 o2o2 Input layer Hidden layer Output layer ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 12 Examples x1x1 x2x o x1x1 x2x o x1x1 x2x2 o OR Network AND Network XOR Network All these neurons use step functions f(