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CE213 Artificial Intelligence – Revision Learning Outcomes Basic AI Concepts and Methods Check List of Problem Solving Skills Sample Questions Useful Equations 1 Learning Outcomes 1. Explain and criticise the arguments that have been advanced both for and against the possibility of artificial intelligence. 2. Explain and implement standard blind and heuristic search procedures, demonstrate an understanding of their strengths and weaknesses and of how they may be applied to solve well-defined problems. 3. Explain the operation of standard production system interpreters, and demonstrate an understanding of their relative merits. 4. Explain the operation of a range of established machine learning procedures and demonstrate an understanding of the types of problems for which they are appropriate. 5. Demonstrate an understanding of the agent-oriented approach to artificial intelligence, and explain how a multi-agent system of purely reactive agents may be built using a subsumption architecture. 2 Basic AI Concepts and Methods AI Search State Space Rep “Generate and Test” Blind Search Heuristic Search Game Playing (problem formalisation) (search methods) (evaluation criteria) Knowledge Representation Machine Learning Importance of Knowledge Production System Forward Chaining Backward Chaining MYCIN Environment for ML ML as Search Decision Tree Induction Neural Networks Clustering Reinforcement Learning Genetic Algorithms (working memory) (rule firing) (certainty factor system) (reasoning with uncertainty) (information gain) ((generalised) delta rule) (discounted cumulative reward) (Q learning) Intelligent Agents Agent Architectures Multiagent Systems (subsumption, …) 3 (interaction, communication) Basic AI Concepts and Methods (2) 1. General AI approach to problem solving: “generate/try + evaluate/test” (actions/solutions) 2. Problem formalisation and knowledge/solution representation: state-action pairs/mapping, sequence of actions/moves, input-output mapping (rules, decision tree, neural net), 3. Search strategies and evaluation criteria: blind and heuristic search strategies forward/backward chaining machine learning as search (including genetic operators) completeness (convergence), optimality, time/space complexity 4. Key elements of a machine learner a task/problem and associated performance measure a model and a learning algorithm training samples (labelled or unlabelled) or scenarios for exploration (learning process) 4 Check List of Problem Solving Skills To formalise a given problem into a state space representation. To analyse the properties of a give search strategy (4 criteria). To find optimal route using a search strategy, given a state space and heuristics or cost. To find best move using minimax search, given a game tree with heuristic values of leaf (terminal) nodes. To identify rules that will fire and draw conclusions using forward chaining, given a set of rules and initial facts. To determine the certainty of a conclusion using backward chaining and the Mycin’s certainty factor system, given a set of rules and initial facts. To calculate information and information gain using the Shannon’s information function, given a set of samples with values of attributes and classes. To induce a decision tree using best attribute selection based on information gain, given a set of samples with values of attributes and classes. 5 Check List of Problem Solving Skills (2) To produce a dendrogram using agglomerative hierarchical clustering method, given a set of samples with values of attributes and similarity metric. To calculate the discounted cumulative reward values of states, given a state transition diagram with transition reward values. To calculate the estimated Q value of a state and an action using the Q learning procedure iteratively, given a state transition diagram with reward value of goal reaching. To choose a proper search strategy, given a specific problem. To choose a proper machine learning method, given a specific learning environment or task. To design an MP neuron (to determine the values of weights and threshold), given a linearly separable 2-dimensional data set. Go through lecture notes and class problem sheets and assignments, and find one exercise to practice for each skill. Tick a box if you are confident in the associated problem solving skill. 6 Sample Questions Check CE213 Exam Paper Rubric and Sample Questions. (http://orb.essex.ac.uk/ce/ce213/CE213 Exam Paper Rubric and Sample Questions.pdf) Check past CE213 exam papers. (http://orb.essex.ac.uk/secure/exampapers.aspx?course=ce213) (http://orb.essex.ac.uk/ce/ce213/CE213-2015-16-answers.pdf) Review/redo the exercises given in the lectures, classes, assignments. (http://orb.essex.ac.uk/ce/ce213/ClassProblemSheetsWithAnswers.zip) Typical problems/applications and problem formalisation: puzzles, game playing, robot control, prediction/forecasting, classification, clustering, optimisation, data mining. 7 Useful Equations CFcombined = CFr1 + (1 – CFr1) x CFr2 (where 0≤CFr1≤1 and 0≤CFr2≤1) () | = | × () = − 8 Useful Equations (2) ( + 1) = () + (! − ") (" # +1 = # + $ ̅ , $ ̅ ⇒ ! − " ( # ) ≡ + , - ., ≡ ., + +) ∗ 1 ., := ., + +) ∗ . 2 := ., + + 34 -(.′, 2 ) 9 Office Hours for CE213 Revision Wednesday 17th May, 2-3pm Thursday 18th May, 2-4pm My office room 4B.524 Answering questions from students …… (For your convenience, a collection of lecture summaries are attached below.) 10 Lecture 1 Summary What is Artificial Intelligence? Building machines that think and learn like people. Building machines that act rationally/intelligently. Is Artificial Intelligence Possible? Lady Lovelace’s Objection The Turing Test Searle’s Chinese Room What Use is Artificial Intelligence? Artificial Intelligence as Technology Artificial Intelligence as Science 11 Lecture 2 Summary Toy problems Corn, Goose and Fox Three Jugs Finding a solution Abstracting the essential features of a problem Systematically searching for a solution State space representation (Problem formalisation) State space Initial and goal states Operators Transition function Representing operators 12 Lecture 3 Summary Blind (Uninformed) Search Strategies • Breadth First Search • Depth First Search • Depth Limited Search • Iterative Deepening Search • Uniform Cost Search Comparison of Search Strategies • Completeness • Optimality • Time Complexity • Space Complexity 13 Lecture 4 Summary Greedy Search Expand node with smallest h(n) Quick but not optimal A* Search Expand node with smallest g(n)+h(n) Optimal (if the heuristic is admissible) Efficient with a good heuristic Hill Climbing Only practical approach in many real problems May not find the global maximum 14 Lecture 5 Summary Minimaxing: Permits adversarial search Evaluation functions (most difficult part): Heuristic on states (game positions) Permit adversarial search in large state spaces alpha-beta pruning (efficiency is essential for a huge search tree): Reduces effort required to search to given depth Monte-Carlo tree search – basic ideas: Evaluation by running simulated games Grandmaster chess programs also use: Complex evaluation function Position databases Specialised parallel hardware 15 Lecture 6 Summary Recognition that knowledge is necessary for problem solving Dendral as an influential example Production systems Situation (condition or state) – action rules Procedural representation Knowledge represented in a form that indicates how it can be used Modularity Production system vs. state space search 16 Lecture 7 Summary Production Rule Interpreters Core components of a production system Rule Set Interpreter Environment (may be embedded in rule set) Forward Chaining Rule Interpreters The Match-Execute Cycle, Use of working memory Conflict Resolution Strategies First match, random, specificity, recency, assigned priority Refractoriness Key Features of Forward Chainers Data driven Rule selected from all those matching current situation Require conflict resolution Iterative 17 Lecture 8 Summary Backward Chaining Rule Interpreters Start with a hypothesis Find rules whose RHSs draw conclusions about that hypothesis. Determine whether the LHSs of those rules match the current situation (This may be recursive with subsidiary hypotheses). If so, execute the corresponding RHSs, thus confirming or rejecting the hypothesis Key Features/Properties of Backward Chainers Hypothesis driven No need for conflict resolution Recursive 18 Lecture 9 Summary Mycin’s basic architecture Representing facts – OAV triples Mycin’s rule format (OAVs in both condition and action parts) Mycin’s control structure (rule interpreter) Representing and reasoning with uncertainty Explanation generation 19 Lecture 10 Summary Why Machine Learning What Is Machine Learning How Is Machine Learning Done Key Elements of a Machine Learner Learning as Search Taxonomy of Learning Tasks Learning to classify, Learning to predict numerical values Clustering, Reinforcement learning • Brief History of Machine Learning • Mathematical Preliminaries (mostly for self study) Probability, Logarithms • • • • • • 20 Lecture 11 Summary Decision Tree Nodes represent attributes; Leafs represent classes; Branches represent attribute values. Decision Tree Induction The Basic Decision Tree Induction Procedure (pseudo code) Choosing the Best Attribute Shannon’s Information Function Using Information Gain to Evaluate an Attribute 21 Lecture 12 Summary The Weather Data Example of Decision Tree Induction Selection of best attributes From Decision Trees to Production Rules Knowledge discovery from data by machine learning Some Issues in Decision Tree Induction Inconsistent Data Numeric Attributes Overfitting (to be addressed again in Neural Networks) 22 Lecture 13 Summary McCulloch-Pitts neural nets MP Unit (Neuron) What Can an MP Unit Compute? Linear Separability More Than One Unit 23 Lecture 14 Summary Learning in Neural Networks Using the Delta Rule Hebb Rule Perceptron Rule The Delta Rule Limitations of the Delta Rule Multilayer Networks Using Linear Units 24 Lecture 15 Summary Multilayer Networks Using Back Propagation Necessity of non-linear units The generalized delta rule Back propagation training of hidden units Overfitting in Back Propagation Networks What is overfitting? How to deal with overfitting? 25 Lecture 16 Summary Clustering What is clustering? Partitioning criteria Agglomerative Hierarchical Clustering Similarity criteria, dendrogram K-Means Method Similarity criteria or distance measures Number of clusters 26 Lecture 17 Summary Reinforcement Learning: Characterising reinforcement learning tasks Markov Decision Processes: Control policies Discounted cumulative rewards Q Learning The Q function The Q learning algorithm (pseudo code) Action selection strategies Learning Q values by neural networks 27 Lecture 18 Summary Biological Basis of Genetic Algorithms: Gene and Chromosome Mutation and Crossover Natural Selection and Fitness A Basic Genetic Algorithm Cycles of “Evaluation – Selection – Reproduction” (a new approach to “generate and test”) Operators for Reproduction Mutation Crossover Genetic Programming (for self study) Syntax Trees Operators (mutation and crossover) 28 Lecture 19 Summary What Are Agents Simple examples of agents Intelligent Agents Pro-active , Conditionally reactive, Socially interactive A Formal View of Agents Reactive agents, Agents with internal state Subsumption Architectures Planetary exploration example Pros and Cons of Reactive Architectures Architecture for Agents with Internal State – BDI Multi-agent Systems Interaction: Cooperation and competition, centralised or distributed Communication: Direct or indirect 29