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  • 1. CS451/CS551/EE565 ARTIFICIAL INTELLIGENCE Learning & Connectionism 12-04-2006 Prof. Janice T. Searleman jets@clarkson.edu, jetsza
  • 2. Outline  Learning Agents  Neural Nets Reading Assignment: AIMA Chapter 18, Learning from Observations Chapter 19, sections 19.1, Logical Formulation of Learning Chapter 20, section 20.5, Neural Networks Final Exam: Mon, 12/11/06, 8:00 am, SC342 HW#7 posted; due: Wed. 12/06/06
  • 3. What is Learning?  memorizing something  learning facts through observation and exploration  improving motor and/or cognitive skills through practice  organizing new knowledge into general, effective representations
  • 4. Some types of learning  Rote learning  Reinforcement learning – an agent interacting with the world makes observations, takes actions, and is rewarded or punished; it should learn to choose actions in such a way as to obtain a lot of reward  Supervised induction (with a teacher) – given a set of input/output pairs, find a rule that does a good job of predicting the output associated with a new input  Unsupervised induction  Analogy (case-based) learning  Evolutionary learning
  • 5. Learning Rote learning Samuel‟s checker program … by taking advice FOO … from problem-solving STRIPS (Shakey) experience … from examples & Winston‟s concept learner counterexamples … by parameter adjustment Samuel‟s checker program … by chunking SOAR … by analogy … by discovery AM/EURISKO Genetic learning Connectionism (Neural Net)
  • 6. Learning agents
  • 7. Learning  The idea behind learning is that percepts should not only be used for acting now, but also for improving the agent‟s ability to act in the future.  In the psychology literature, learning is considered one of the keys to human intelligence.
  • 8. Learning element  Design of a learning element is affected by  Which components of the performance element are to be learned  What feedback is available to learn these components  What representation is used for the components  Type of feedback:  Supervised learning: correct answers for each example  Unsupervised learning: correct answers not given  Reinforcement learning: occasional rewards
  • 9. Rote Learning  This is the simplest form of learning.  It involves nothing more than memorizing experiences, e.g., sensor inputs, actions taken, rewards received.  It‟s surprisingly effective!
  • 10. Machine learning programs for classification (concept learning)  Assume you have a goal concept that you are trying to learn, called the target concept. Your guesses or approximations of the target concept are called hypotheses.  Anexample target concept might be a description of diseased soybean plants.  An object (fact) which is used to help learn the goal concept is called an instance or an example. It can also be called a case.  An instance/example x is described by a vector of features, also called attributes , i.e., x = <x1,……,xn>.
  • 11. Classification tasks  Many engineering and diagnosis tasks involve classification or prediction, e.g.,  Parts inspection: classify parts into: defective or OK.  Mammogram analysis: given a mammogram, estimate the probability that is normal, pre-cancerous or cancerous.  Document understanding: given a rectangular region from a scanned region, classify it as text or graphics.  Soybean plant analysis: classify plants into: diseased or not diseased.
  • 12. Machine learning programs for classification (concept learning)  Given: A labeling function f that maps feature vectors into a discrete set of k classes. That is, f(x) in {0,1,……,k-1}. Often, there are only 2 classes, called “positive” (+) and “negative” (-).  Represent each training example as a pair (x,f(x)). These are the examples that will be used for learning the concept.  Problem: From a set of (x,f(x)) pairs, learn the target concept f.
  • 13. The learning problem  Given <x,f(x)> pairs, infer f x f(x) Given a finite sample, it is often 1 1 impossible to guess the true function f. 2 4 3 9 Approach: Find some pattern (called a hypothesis) in 4 16 the training examples, and assume 5 ? that the pattern will hold for future examples too.
  • 14. Hypothesis or model selection  What class of hypotheses (models) should we consider?  Assume f is a set of rules. Then the space of hypotheses consists of rule sets.  Assume f is a simple polynomial. Then the space of hypotheses consists of simple polynomials. Regression could be used to learn f.  Assume f can be expressed as a decision tree. Then the space of hypotheses consists of decision trees. Decision tree learning can be applied.  Assume f can be expressed as a neural network. Then the space of hypotheses consists of neural nets, and backprop can be used to learn the weights.  Let H be the space of all possible hypotheses H that a learning program considers. Then the learner seeks the H in H that “fits” the given data the “best.” This is a process of search through the space of possible hypotheses in H.
  • 15. Learning decision trees Problem: decide whether to wait for a table at a restaurant, based on the following attributes: 1. Alternate: is there an alternative restaurant nearby? 2. Bar: is there a comfortable bar area to wait in? 3. Fri/Sat: is today Friday or Saturday? 4. Hungry: are we hungry? 5. Patrons: number of people in the restaurant (None, Some, Full) 6. Price: price range ($, $$, $$$) 7. Raining: is it raining outside? 8. Reservation: have we made a reservation? 9. Type: kind of restaurant (French, Italian, Thai, Burger) 10. WaitEstimate: estimated waiting time (0-10, 10-30, 30- 60, >60)
  • 16. Attribute-based representations  Examples described by attribute values (Boolean, discrete, continuous)  E.g., situations where I will/won't wait for a table:  Classification of examples is positive (T) or negative (F)
  • 17. Connectionism
  • 18. Recall: Physical Symbol System  A physical symbol system is a machine that produces through time an evolving collection of symbol structures. Such a system exists in a world of objects wider than just these symbolic expressions themselves. The Physical Symbol System Hypothesis  A physical symbol system has the necessary and sufficient means for general intelligent action. PSS => Intelligence Intelligence => PSS Well…maybe ???
  • 19. Symbols & The Main Goals of AI  Engineering: Build intelligent systems  Lots of fantastic symbolic AI systems for a multitude of specialized tasks….and many more to come!  But general intelligent systems are a major problem, since common sense is hard to represent and reason with symbolically.  Science: Understand natural intelligence via computers  Cognitive Science founded by symbolic AI researchers.  But they took the metaphor too far.  Organisms clearly compute, but not necessarily:  as Von Neumann computers (i.e. serially)  as Logic theorem provers (i.e. mathematically complete & consistent)  with symbols!! …We can interpret the reasoning process as symbolic, but the underlying mechanism may not be. The ends don‟t explain the means!
  • 20. The Intelligence Spectrum  Robot (Moravec ,1999) Calculate Reason Sense & Act Humans Computers ■ On the fringes:  Humans are slow, error-prone calculators  Robots sense and act no better (and much slower) than frogs ■ The battle for middle ground:  Deep Blue beat the best human chess player  But minimax search ≠ “reasoning”.
  • 21. GOFAI -vs- The New AI Calc Reason Sense & Act GOFAI New AI  GOFAI (“Good Old Fashioned AI) ■ Disembodied reasoning systems can‟t plug-and-play on robots. ■ Lack of common sense => no general human reasoning abilities.  New AI ■ Embodied S&A gives basis for common sense but has not yet scaled up to sophisticated human-like abstract reasoning.
  • 22. Evolutionary Progressions along the Intelligence Spectrum Living organisms Computers Sense & Act: 10,000,000+ years. 15+ years Reason: 100,000+ 30+ years Calculate 1,000+ 50+ years  Evolution of reasoning was tightly constrained and influenced by sensorimotor capabilities. Else extinction!  GOFAI systems are often in their own little worlds, making unreasonable assumptions about independent sensorimotor apparatus.  To achieve AI‟s scientific goal of understanding human intelligence, the road from sense-and-act to reasoning via simulated evolution may be the only way.  But, to achieve AI‟s engineering goals, both approaches seem important.
  • 23. The Situated & Embodied AI Hypothesis  Complex intelligence is better understood and more successfully embodied in artifacts by working up from low-level sensory-motor agents than from abstract cognitive mechanisms of rationality (e.g. logic, means-ends analysis, etc.).  Cognitive Incrementalism: Cognition (and hence common sense) is an extension of sensorimotor behavior. Brooks, Steels, Pfeifer, Scheier, Beer, Nolfi, Floreano…
  • 24. The Artificial Life Approach to AI Simple Robots Cellular Automata 22222222 2170140142 2022222202 272 212 Simulated Real Worlds 212 212 202 212 272 212 21222222122222 20710710711111 2222222222222 Langton Loop ■ Synthetic: Bottom-up, multiple interacting agents ■ Self-Organizing: Global structure is emergent. ■ Self-Regulating: No global/centralized control. ■ Adaptive: Learning and/or evolving ■ Complex: On the edge of chaos; dissipative
  • 25. Adaptation  Key focus of Situated & Embodied AI (i.e., Alife AI)  But now, often at level of simple organisms (ants, flies, frogs, etc.)  Machine Learning (ML) is also a key part of GOFAI.  Alife AI is very interested in subsymbolic ML techniques:  Artificial Neural Networks (ANNs)  Evolutionary Algorithms (EAs)  Learning: agents modify their own behavior (normally to improve performance) in their lifetime.  Evolution: populations of agents change their behavior over the course of many generations.  Both: Evolving populations of learning agents
  • 26. Neural Networks  Also „connectionism‟, „Parallel Distributed Processing‟, „subsymbolic AI‟  AI technique  Analogous to processes in the brain  “Intelligence emerges from the interactions of large numbers of simple processing units” (Rumelhart et al., 1986)  Roughly based on brains – some simplification is made
  • 27. Real Neuron from Searleman & Searleman, Introduction to Cognition
  • 28. Two interacting neurons  Excitatory (E) and Inhibitory (I) impulses (from Searleman & Searleman, Introduction to Cognition)
  • 29. Neurons NeuroPhysiology Synapses Axon Dendrites • Dense: Human brain has 1011 neurons, 1014 synapses • Highly Interconnected: Human neurons have 104 fan-in. • Neurons firing: send action potentials (APs) down the axons when sufficiently stimulated by SUM of incoming APs along the dendrites. • Neurons can either stimulate or inhibit other neurons. • Synapses vary in transmission efficiency
  • 30. Features of the human brain  Robust – fault tolerant and degrade gracefully  Flexible -- can learn without being explicitly programmed  Can deal with fuzzy, probabilistic information  Is highly parallel
  • 31. Connectionist models  Key intuition: Much of intelligence is in the connections between the 10 billion neurons in the human brain.  Neuron switching time is roughly 0.001 second; scene recognition time is about 0.1 second. This suggests that the brain is massively parallel because 100 computational steps are simply not sufficient to accomplish scene recognition.  Development: Formation of basic connection topology  Learning: Fine-tuning of topology + Major synaptic- efficiency changes. The matrix IS the intelligence!

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