Remembering and Learning

1. Remembering and Learning

2. Recap of Chapter 3 • Mostly puzzle problems requiring little knowledge to solve. • “The simplicity we discovered there was largely a simplicity of process and a simplicity of the architecture of the mind.” • Simple process • Few things to know and not many potential actions • Simple architecture • Few parameters, such as capacity of STM and storage time for LTM • What do we see when we examine complex domains

3. Semantically-Rich Domains • Simon assumes “small repertory of information processes of the sorts described in the last chapter” • Using • Input from eyes, ears, touch, etc. • Output via legs, hands, tongue • Large store of correct and incorrect information about that world • Need to understand memory

4. Library Metaphor • LTM as a library • Vast collection of information organized by topic • Cross-references between items (associations) • Elaborate index enabling recognition • LTM operates as second environment that • Can be searched for information • Can be the focus of reflection/reaction

5. Outer and Inner Search • Two diagnostic practices of physicians • Direct recognition – presence of symptom(s) leads to hypothesis • Search – similar to description of general problem solving (e.g. use evidence to select additional tests to gather more information) • Search is conducted simultaneously in mind of physician and body of patient • How is this like McGuckins? • Intuition as recognition, or compiled knowledge?

6. How Much Information? • A professional’s knowledge is adequate when she knows about as much as other professionals in her domain. • Time available to acquiring and maintaining knowledge will affect limit for large domains • Describing expertise • ~50,000 chunks across disciplines, or 10 years of learning • When a domain exceeds this • It will increase use of externalized information stores • It will divide into subfields (specialize) • Science proceeds through producing new knowledge and compressing old through more general theories

7. Alternative Representations • Stored as data • Memory of information • Can think of databases or indexed document stores • Examples of data you know as a CS expert? • Stored as process • Memory of processes for determining information • Can think of production rules, • Examples of process you know as a CS expert?

8. Understanding and Representation • Representing word problems • Do you recognize the Tea Ceremony problem? • As described, it is about the assignment of tasks to actors and constraints on how tasks can be transferred • Early programs that “understood” • UNDERSTAND – basic NLP for creating objects and relations from text • ISAAC – filling in existing schema based on parsed text • Does vast size of store necessitate complexity • Can still be thought of as simple process on big data • But increase in size can increase the number of types of relations, constraints, and interactions that must be considered during problem solving

9. Learning • Distinguish between acquiring information (data) and acquiring skills (process) • Learning by rote vs. learning with understanding • Rote learning can be regurgitated but not used as tool • Learning with understanding is faster, lasts longer, and is more generally applicable • Differences in indexing, redundancy, and representation • How many stars were on the US flag in 1940? • How did you answer that?

10. Production (rule-based) Systems • Easier to generate new data representations and add data than to add new processes • Programming is hard • Production systems use simple encoding of process as data • Condition -> Action • Production rules can be used in multiple ways • Governed by perception (stimulus or data driven) • Forward chaining • Governed by goal (goal driven) • Often backward chaining • Adaptive production system – where productions are added, deleted, or modified during runtime • Writing code that writes code • Writing code that modifies itself?

11. Learning from Examples • Can take steps in example and generalize to process description (e.g. production rules) • How to generalize at right level? • Learning by doing for learning process • Discussions of understanding and learning relate to constructivist educational pedagogy • Learning with understanding better than learning by rote + Learning process more applicable than learning data = Learning by example/doing

12. Discovery • Discover is just learning • “what constitutes novelty depends on what is already in the mind of the problem solver” • Discovery does not have a goal • AM used criteria for judging how interesting a pattern is to produce findings • BACON located monotonically varying data and introduced new concepts to represent relations found • DENDRAL and MECHEM generated publishable results (was this the beginning of “Big Data”) • Success in discovery is related to selected representation • Focus of attention determined by representation • E.g. mutilated checker board

13. Simon Sticks to His Guns • He continues to argue that human beings can be viewed as simple problem solving systems • Provided that • “we include in what we call the human environment the cocoon of information, stored in books and in long-term memory, that we spin about ourselves.