Knowledge Representation Reading: Chapter

2 Classes offered in spring  Vision/Robotics  6732 Computational Imaging (Nayar)  6735 Visual Databases (Kender)  Computational Photography (Belhumeur)  NLP/Speech  4706 Spoken Language Processing (Hirschberg)  Natural Language Processing for the Web (McKeown)   Machine Learning  4771 Machine Learning (Jebara)  Other  D User Interfaces (Feiner)

3 Homework  What’s important (i.e., this will be used in determining your grade): Finding features that make a difference You should expect to do some digging in the data Find a feature that requires manipulation of data Reformatting of data to provide a more consistent feature (e.g., gender, profession)  Turn in a sample of your data file in ARFF format with the features you ended up using (5 instances only)  Turn in a Weka log documenting the series of steps you used to arrive at your model We want the experimentation that backs up your claims in the report

4  When you notice a cat in profound meditation, The reason, I tell you is always the same: His Mind is engaged in a rapt contemplation of the Thought, of the thought, of the thought of his name. T.S. Eliot Old Possum’s Book of Practical Cats

5 Five Roles that KR plays  A surrogate for some part of the real world  A set of ontological commitments  A fragmentary theory of intelligent reasoning  A medium for pragmatically efficient computation  A medium of human expression

6 KR as a surrogate  Agents “reason” about models of the world, rather than the world itself  Deduce properties without having to directly gather information from the world  Predict consequences of potential actions rather than performing the actions directly

7  We always have two universes of discourse – call them “physical” and “phenomenal”, or what you will – one dealing with questions of quantitative and formal structure, the other with those qualities that constitute a “world.” All of us have our own distinctive mental worlds, our own inner journeyings and landscapes, and these, for most of us, require no clear neurological “correlate.”

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13 Given a representation  What are its semantics?  What is the meaning of its structures?  What does it mean/refer to?  Fidelity – how accurate is it?

14 Areas of Activity  Designing formats for expressing information  Mostly “general purpose” knowledge representations (e.g., first order logic)  Encoding knowledge (knowledge engineering)  Mostly identifying and describing conceptual vocabulary (ontologies)  Declarative representations are the focus of KR technology  Knowledge that is domain specific but task independent

15 Example of representations

16 KRs are never a complete model  When modeling the real world, KRs are always imperfect  “Consequently, even with a sound reasoning, incorrect conclusions are inevitable”

17 Ontological commitments  A KR is a set of ontological commitments  An ontology is a theory of what exists in the world  Classes, objects, relations, attributes, properties, constraints, special individuals, etc.  Provides a vocabulary for expressing knowledge

18 Example of KR structures

19 A Vocabulary for the World  A KR makes a commitment to a particular ontology  To describing the world with particular terms  Taxonomy of the world  Promiscuity vs. perspicacity

20 Example of a Vocabulary for the World

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23 Ontological Commitments  “The commitments are in effect a strong pair of glasses that determine what we can see, bringing some part of the world into sharp focus, at the expense of blurring other parts.”  A KR is not just a data structure “Part of what makes a language representational is that it carries meaning, I.e., there is a correspondence between its constructs and things in the external world.”

24 KR as a theory of reasoning  Many knowledge representations offer fragmentary theories of intelligent reasoning  Humans employ multiple strategies for representing and reasoning about the world

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26 Impact of reasoning theory  The selected theory affects methods and possible inference  Only certain facts can be inferred  Some methods of inference are “sanctioned” or illegal  A better method of reasoning than undirected search  Theory provides “recommendations” for strategies of inference

27 Efficient Computation  Some work has focused on knowledge content and what could, in principle, be derived from it without concern for efficiency  Sound  Complete  Tradeoff between efficiency and expressiveness

28 Heuristic Adequacy  Providing a representation that supports adequately efficient problem solving  Early heuristic systems  Any-time computations

29 KR as a medium for human expression  An intelligent system must have KRs that can be understood by humans  We need to be able to encode knowledge in the knowledge base without significant effort  We need to be able to understand what the system knows and how it draws it conclusions

30 Open Issues for KR  How can a reasoning mechanism generate implicit knowledge?  How can a system use knowledge to influence its behavior?  How is incomplete or noisy knowledge handled?  How can practical results be obtained when reasoning is intractable?

31 Different Forms of Knowledge Representation  Logical representation schemes  Procedural representation schemes  Network representation schemes  Structured representation schemes