Ch1-Introduction to computation Intelligence.pptx
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Here are 3 assignments related to the document: 1. Analyze Turing's test for computer intelligence and his prediction that computers could pass a 5-minute version with 70% probability given 109 bits of storage. 2. Evaluate the 1996 IEEE Neural Networks Council's definition of artificial intelligence in relation to computational intelligence paradigms. 3. Demonstrate how each computational intelligence paradigm—artificial neural networks, evolutionary computation, swarm intelligence, artificial immune systems, and fuzzy systems—satisfies the provided definition of computational intelligence.
Ch1-Introduction to computation Intelligence.pptx
- 1. Knowing is not enough; we must apply. Wishing is not enough; we must do.
- 3. A.I • what is intelligence? • Dictionaries define intelligence as the ability to comprehend, to understand and profit from experience, to interpret intelligence, having the capacity for thought and reason • Can computers be intelligent? • Alan Turing gave much thought to this question. He believed that machines could be created that would mimic the processes of the human brain. • Turing strongly believed that there was nothing the brain could do that a well-designed computer could not. More than fifty years later his statements are still visionary.
- 5. Computational Intelligence Paradigms • ANNs- Artificial Neural Networks: Biological Neural Systems • SI- Swarm Intelligences: Social Behavior of organisms living in swarms or colonies • EC- Evolutionary Computations: Natural Evolution • AIS- Artificial Immune Systems: the human immune system • FS- Fuzzy Systems: studies of how organisms interact with their environment.
- 6. Artificial Neural Networks • The brain is a complex, nonlinear and parallel computer • It is estimated that there is in the order of 10-500 billion neurons in the human cortex, with 60 trillion synapses. • The neurons are arranged in approximately 1000 main modules, each having about 500 neural networks.
- 7. Artificial Neural Networks • An artificial neuron (AN) is a model of a biological neuron (BN). • Each AN receives signals from the environment, or other ANs, gathers these signals, and when fired, transmits a signal to all connected ANs. • The firing of an AN and the strength of the exiting signal are controlled via a function, referred to as the activation function. • The AN collects all incoming signals, and computes a net input signal as a function of the respective weights. • The net input signal serves as input to the activation function which calculates the output signal of the AN.
- 8. An artificial neural network (NN) • An artificial neural network (NN) is a layered network of Ans • An NN may consist of an input layer, hidden layers and an output layer. • ANs in one layer are connected, fully or partially, to the ANs in the next layer. • Feedback connections to previous layers are also possible.
- 9. Types of NNs • Single-layer nns, such as the Hopfield network • Multilayer feedforward nns, including, for example, standard backpropagation, functional link and product unit networks; • Temporal nns, such as the elman and jordan simple recurrent networks as well as time-delay neural networks; • Self-organizing nns, such as the kohonen self- organizing feature maps and the learning vector quantizer; • Combined supervised and unsupervised nns, e.g. Some radial basis function networks.
- 10. Application of ANNs • Diagnosis of diseases • Speech recognition • Data mining • Composing music • Image processing, • Forecasting • Robot control • Credit approval • Classification • Pattern recognition • Planning • Game strategies • Compression
- 11. Evolutionary Computation • Evolutionary computation (EC) has as its objective to mimic processes from natural evolution, where the main concept is survival of the fittest: the weak must die. • In natural evolution, survival is achieved through reproduction. • Offspring, reproduced from two parents (sometimes more than two), contain genetic material of both (or all) parents – hopefully the best characteristics of each parent.
- 12. Different classes of evolutionary algorithms (EA) • Genetic programming which is based on genetic algorithms, but individuals are programs (represented as trees). • Evolutionary programming which is derived from the simulation of adaptive behavior in evolution (phenotypic evolution). • Evolution strategies which are geared toward modeling the strategy parameters that control variation in evolution, i.e. the evolution of evolution. • Differential evolution, which is similar to genetic algorithms, differing in the reproduction mechanism used. • Cultural evolution which models the evolution of culture of a population and how the culture influences the genetic and phenotypic evolution of individuals. • Coevolution where initially “dumb” individuals evolve through cooperation, or in competition with one another, acquiring the necessary characteristics to survive.
- 13. Application of EC • Data mining • Combinatorial optimization • Fault diagnosis • Classification • Clustering, • Scheduling • Time series approximation
- 14. Swarm Intelligence • Swarm intelligence (SI) originated from the study of colonies, or swarms of social organisms. • Studies of the social behavior of organisms (individuals) in swarms prompted the design of very efficient optimization and clustering algorithms. • For example • simulation studies of the graceful, but unpredictable, choreography of bird flocks led to the design of the particle swarm optimization algorithm, and studies of the foraging • behavior of ants resulted in ant colony optimization algorithms.
- 15. Types of SI • Particle swarm optimization (PSO) is a stochastic optimization approach, modeled on the social behavior of bird flocks • Colony optimization Algorithm are studies of ant colonies, have contributed in abundance to the set of intelligent algorithms. • Clustering and structural optimization algorithms are studies of the nest building of ants and bees
- 16. Applications of SI • Routing optimization • In telecommunications networks • Graph coloring • Scheduling • Solving the quadratic assignment problem.
- 17. Artificial Immune Systems • The natural immune system (NIS) has an amazing pattern matching ability, used to distinguish between foreign cells entering the body (referred to as non-self, or antigen) and the cells belonging to the body (referred to as self). • As the NIS encounters antigen, the adaptive nature of the NIS is exhibited, with the NIS memorizing the structure of these antigen for faster future response the antigen.
- 18. NIS Models • The classical view of the immune system is that the immune system distinguishes between self and non- self, using lymphocytes produced in the lymphoid organs. These lymphocytes “learn” to bind to antigen. • Clonal selection theory, where an active B-Cell produces antibodies through a cloning process. The produced clones are also mutated. • Danger theory, where the immune system can distinguish between dangerous and non-dangerous antigen. • Network theory, where it is assumed that B-Cells form a network. When a B-Cell responds to an antigen, that B-Cell becomes activated and stimulates all other B-Cells to which it is connected in the network.
- 19. AIS applications • Feature extraction • Pattern recognition • Memory • Learning • Classification • Adaption for utilization in computer security • Fraud detection • Machine learning • Data analysis • Optimization algorithms
- 20. Fuzzy Systems Fuzzy sets and fuzzy logic allow what is referred to as approximate reasoning. With fuzzy sets, an element belongs to a set to a certain degree of certainty. Fuzzy logic allows reasoning with these uncertain facts to infer new facts, with a degree of certainty associated with each fact.
- 21. Types of Uncertainty in FLS Statistical uncertainty is based on the laws of probability Nonstatistical uncertainty is based on vagueness, imprecision and/or ambiguity
- 22. Applications of FLS • Applied successfully to control systems • Facial pattern recognition • Air conditioners • Washing machines • Vacuum cleaners • Antiskid braking systems • Transmission systems • Control of subway systems • Unmanned helicopters • knowledge-based systems
- 23. Assignments 1. Comment on the eligibility of Turing’s test for computer intelligence, and his belief that computers with 109 bits of storage would pass a 5-minute version of test with 70% probability. 2. Comment on the eligibility of the definition of artificial intelligence as given by the 1996 IEEE Neural Networks Council. 3. Based on the definition of CI given in this chapter, show that each of the paradigms (NN, EC, SI, AIS, and FS) does satisfy the definition