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Decision making with Case-Based Learning

Decision making with Case-Based Learning. Instructor Dr. George Collins By Ratna Prasad Kandi. Table of contents. Introduction Case based decision theory. Case analysis of CBDT Cognitive engine architecture Memory Forgetfulness

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Decision making with Case-Based Learning

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  1. Decision making with Case-Based Learning Instructor Dr. George Collins By Ratna Prasad Kandi

  2. Table of contents • Introduction • Case based decision theory. • Case analysis of CBDT • Cognitive engine architecture • Memory • Forgetfulness • Cognitive engine CBDT implementation • Sensors • Conclusion

  3. Introduction • In cognitive radio design, decision making is a complex part. • Cognitive radio adapts itself by making some decisions. • This decisions are based on user requirements or based on environmental and behavioral information. • Decisions based on the past knowledge.

  4. Introduction • Same as how people solve problems. • Previous knowledge influences current decisions. • Anytime learningin changing problem spaces. • CBDT(case based decision theory).

  5. Case based decision theory Uses sensors to observe when the environment or user needs change. New information is modeled as new problem to be solved by cognitive radio.  Case based system analyzes the new problem against past cases in the memory.  To determine the similarities between the new problem and past problems.

  6. Case based decision theory Need to determine the utility of the past actions. Similarity function: • Defines how similar two cases are. • Measures how two cases are close i.e. ‘0’ represents no similarity and 1 represents perfect match. Utility function: How successful was an action responding to a problem.

  7. Case analysis of CBDT • For a case based learning let set of problems be P, set of actions be A, set of results be R Then case C= P*A*R. • Decision is based on the function U(a)=s(p,q)u(r).

  8. Cognitive Engine Architecture • The case base and optimization routers work together to enable learning and adaptation in cognitive engine. • The case base holds past cases, actions and results of the action. • The results are measurement of how well the action is performed.

  9. Memory case base holds M cases. • It must have a system to delete or forget cases which are no longer used. • When cognitive engine observes a new case, either the new case is not remembered or it must replace current case. • To replace current cases we have forgetfulness functions.

  10. Forgetfulness • Temporal forgetfulness: 1) In this method the oldest case is forgotten. 2) Simple to implement using FIFO buffer. Maximum distance forgetfulness: Two cases far away from each other are forgotten. Maximum utility forgetfulness: In this case with lowest utility is replaced by new case.

  11. Cognitive engine CBDT Implementation • The new case is received by the cognitive controller through a sensor. • The cognitive controller calls the decision making process to find the information in the knowledge, or case ,base. • Then sends information to the optimizing routine for processing. • Each component can be developed and implemented independently.

  12. Sensors • Cognitive Engine associates with three sensors to collect the meters, PSD and objectives. • The wireless system genetic algorithm does the optamization. • The PSD sensor provides an interference map that the GA uses in analyzing its objectives. • The case based decision maker uses the meters sensor in its utility calculations.

  13. Example • Take the case of wireless communication standards. • Mobile phones provide different protocols like WiFi, Bluetooth, LTE, WiMax etc. • Based on the usage by the user wireless radios switch between different protocols. • The decision maker must be able to determine which service to use for current application.

  14. Conclusion • Case based decision theory as the mechanism for providing the feedback. • When new problem is received ,the decision maker looks for similar problem in case base that exhibit high utility. • Utility function can have greater impact on the performance. • Case based learning provides significant potential for use with the cognitive engine where problem requires solutions quickly as situations and environment changes.

  15. References Artificial Intelligence in Wireless Communications. Thomas Warren Rondeau, Charles W. Bostian. • I.Gilboa and D.Schmeidler, “ A Theory of case based Decisions”. • C.L Ramsey and J.J Grefenstette, “Case-based decisions” • D.E Goldberg, Genetic algorithm in search, optimization and machine learning.

  16. THANK YOU

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