remembering and learning
Post on 09-Feb-2016
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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.”
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