Remembering and Learning

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

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

Long-Term Memory LTM as a library Vast collection of information organized by topic/theme/etc. 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

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?

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

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?

Understanding and Representation Representing word problems Did 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

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?

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?

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

Discovery Discovery is a type of 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

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.”