Artificial Intelligence (AI) Common AI Methods. Training. Signals to Perceptrons. Artificial Neural Networks (ANN) Artificial Intelligence
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1 Artificial Intelligence (AI) Artificial Intelligence AI is an attempt to reproduce intelligent reasoning using machines * * H. M. Cartwright, Applications of Artificial Intelligence in Chemistry, 1993, pg. 2, Oxford University Press, Oxford CHEM8711/7711: Artificial Intelligence 2 Common AI Methods Artificial Neural Networks: utilize a computational model of the brain (multiple interconnected neurons) in order to learn Expert Systems: utilize a knowledge base and set of rules (heuristics) in order to provide expert assistance Genetic Algorithms: utilize the concepts of evolution to produce good solutions to a problem from poor random initial guesses CHEM8711/7711: Artificial Intelligence 3 Artificial Neural Networks (ANN) ANN uses perceptrons to mimic the functions of simple neurons: Has multiple input connections (s i ) -> adds up signals arriving on these connections (Σ i w i s i ) Remains off unless the sum reaches a threshold (Θ) Returns to off state after a short time CHEM8711/7711: Artificial Intelligence 4 Signals to Perceptrons Training Training involves both a training stimulus (input to the ANN) and a training target (the desired output) Perceptron learning rules: If output is correct, do nothing If incorrect on signal is given, decrease weights on active inputs If incorrect off signal is given, increase weights on active inputs CHEM8711/7711: Artificial Intelligence 5 CHEM8711/7711: Artificial Intelligence 6
2 Pattern Recognition A Simple NN Simple perceptrons can be trained to recognize simple patterns (molecules containing rings) The ability to train a perceptron is dependent on having a common ring orientation and size Recognition of rings having different orientations and sizes requires a network of perceptrons (ANN) More generally, problems must be linearly separable for a single perceptron to handle CHEM8711/7711: Artificial Intelligence 7 CHEM8711/7711: Artificial Intelligence 8 Example Problem Monitor temperature (T) and ph in a reaction vessel and sound an alarm if T>95 OR ph<4.5 This is not a single linearly separable problem, but is a conjunction of two linearly separable problems Solving the Problem A minimum of two perceptrons are required One monitors temperature and ignores the ph signal The other monitors ph and ignores the temperature signal CHEM8711/7711: Artificial Intelligence 9 CHEM8711/7711: Artificial Intelligence 10 Training Networks Networks involve multiple layers of perceptrons, but only the target signals of the output perceptrons are known Backpropagation Collects errors from the output perceptrons Errors are divided among the various connections in the network The weights for those connections are adjusted in order to reduce the error A single network can be trained for multiple applications Fault tolerance ANNs handle noisy data reasonably well Trained networks can deal with previously unseen data ANN operate in parallel ANN discover new relationships among input data ANN can cope with fuzzy data Selection of training set determines quality of training Relationships discovered by ANN are not readily translated into human understanding CHEM8711/7711: Artificial Intelligence 11 CHEM8711/7711: Artificial Intelligence 12
3 Another Neural Net Example Expert Systems Prediction of optimal conditions for protein crystallography in current use at the Center for Biophysical Sciences and Engineering at UAB Robotics used to set up hundreds of crystal growth experiments condition combinations determined by N- factorial analysis growth conditions (ph, concentration, etc) used as input for a back-propagation neural network crystal quality (manually graded on 1-10 scale) used as output using first round crystallization trials, can reliably predict optimal untested combination of conditions to produce highest quality crystals CHEM8711/7711: Artificial Intelligence 13 CHEM8711/7711: Artificial Intelligence 14 Searching the Knowledge Base CHEM8711/7711: Artificial Intelligence 15 Can be used in situations where an expert is not available Can collect input from the user and combine with knowledge base to infer solutions Not applicable to new situations Requires considerable expert input to develop Expert knowledge may not easily be easily fed into the knowledge base Requires constant updates in highly active areas (example: synthetic planning) CHEM8711/7711: Artificial Intelligence 16 Genetic Algorithm (GA) GA Applicability Initialize Population of Starting Solutions Select Fit Population Members Breed New Problem Solutions Many optimization algorithms go up (or down) hills Could be found by GA Would usually be found by hill climbing method GA explores from multiple starting points Generate Next Generation Population CHEM8711/7711: Artificial Intelligence 17 CHEM8711/7711: Artificial Intelligence 18
4 Representing a Solution Br H O b a N H Binary Representation: Solution Representation Requires 8 Genes: 1. Dihedral Angle Labeled a 2. Dihedral Angle Labeled b 3. Rotation of entire molecule around x axis relative to standard orientation 4. Rotation of entire molecule around y axis relative to standard orientation 5. Rotation of entire molecule around z axis relative to standard orientation (Binary example uses 3 bits to encode 360 degrees at 45 degree increments) 6. Distance in x direction from standard orientation 7. Distance in y direction from standard orientation 8. Distance in z direction from standard orientation (Binary example uses 3 bits to encode from -2.0 to +1.5 in 0.5 Å increments) Real-Valued Representation Binary Mutation Concern Few single mutations result in small changes to the actual angle value Angle Value Binary Code Value Gray Code Value CHEM8711/7711: Artificial Intelligence 19 CHEM8711/7711: Artificial Intelligence 20 Reproduction: Crossover Reproduction: Mutation Parent 1 Parent Binary Representation: Child (Uniform) crossover Mutate bit Children (One-Point) Real-Valued Representation Children (Two-Point) CHEM8711/7711: Artificial Intelligence 21 Mutate real value CHEM8711/7711: Artificial Intelligence 22 GA Behavior Operates in parallel Able to solve problems with complex landscapes Requires ability to represent problem as a string The solution must be assembled from segments (schema) that confer high fitness CHEM8711/7711: Artificial Intelligence 23 CHEM8711/7711: Artificial Intelligence 24
5 Related Reading CHEM8711/7711: Artificial Intelligence 25
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