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LEARNING MODEL FOR CONSTRUCTION OF THE BEST DECISION SEQUENCE USING PRIOR KNOWLEDGE

LEARNING MODEL FOR CONSTRUCTION OF THE BEST DECISION SEQUENCE USING PRIOR KNOWLEDGE. Thesis Presentation. by Lilit Yenokyan. PROBLEM STATEMENT.

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LEARNING MODEL FOR CONSTRUCTION OF THE BEST DECISION SEQUENCE USING PRIOR KNOWLEDGE

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  1. LEARNING MODEL FOR CONSTRUCTION OF THE BEST DECISION SEQUENCE USING PRIOR KNOWLEDGE Thesis Presentation by Lilit Yenokyan

  2. PROBLEM STATEMENT • The task of this thesis is development of a learning system capable of learning from prior solution instances of a problem and able of developing new solution procedures when similar problem is introduced to the system. • System involves multiple decision makers and multiple decision sequences.

  3. PROBLEM STATEMENT (cont.) • Applications • Medical Field • Product Assembly • Assisting Drivers in Navigation • Other tasks presented in the terms of system (Decision Makers, Decision sequences)

  4. Stages of the Approach • Constructing the Knowledge Base • Initial approach builds a learning system to document the steps of decision sequences in problem solving and also information/data used for decision making. • Directed Acyclic Graph (DAG) is used to represent these decision sequences, where the nodes in the graph represent the actions taken by the problem solver and directed edges represent the order of the decisions (steps). • In mathematics DAG, is a directed graph with no directed cycles; such that, for any vertex v, there is no nonempty directed path that starts and ends on v.

  5. Stages of the Approach • Finding the Best Decision Sequence • Dynamic programming approach is proposed to quickly examine various solution alternatives documented in DAG and develop as new improved solution for the problem. • The notion of “best” solution is based on the problem and the application, e.g. shortest completion time, shortest distance traveled, least load or cost required and so on.

  6. Available Learning Models • Artificial Intelligence Agent – the actor in the system. • Multi-agent Systems • Cooperative • Concurrent • Supervised • Correct output is provided to system. • Unsupervised • Ability of recognize patterns in streams of input • Reinforcement • ‘Reward’ for correct decisions, ‘punishment’ for incorrect decisions • Semi-Supervised Learning • Combination of Supervised and Unsupervised Learning

  7. Available Learning Models (cont.) • Neural Networks Inspired and modeled based on brain neurons. • Feed-Forward • Recurrent Neural Networks • Decision Trees • Classification Learning. Every branch is a chose between a number of alternatives, and each leaf node represents a classification or decision. • Explanation Based Learning • Uses learning model of an example is sufficient for learning. • Relevance Based Learning • Generalizes the information from prior knowledge and uses hypothesis to create new learning examples

  8. Differences and similarities • Our approach includes elements of • Reinforcement Learning • Semi-Supervised Learning • Decision Trees Using Methods: • Graph Theory • Dynamic programming

  9. Notations

  10. Types of nodes Knowledge base presented as Directed Acyclic Graph with distinct types of nodes NodeSets and Regular Nodes is called Hypertree.

  11. Introducing concept of NodeSet Decision that results from merging nodes i1 and i2 with attribute x, is the outcome A

  12. Introducing concept of NodeSet (cont.) Decision that results from merging nodes i3 and i4 with attribute y, is the outcome A

  13. Introducing concept of NodeSet (cont.) NodeSet notation used to illustrate the case: and.

  14. Example – Two Decision sequences First Decision Sequence

  15. Example – Two Decision sequences Second Decision Sequence

  16. Theory and Algorithms

  17. Knowledge Base Construction Algorithm Decision made by DM N, where A is resulting decision {e1, …, en} are the decision at the input that produce A, {a} isthe attribute value of the decision. Decision sequence of decision maker N. NodeSet in the Hypertree, where • A is resulting intermediate or final entity, • {e1, …, en} are the basic or intermediate entities at the input that produce A, • {a} isthe attribute value of the nodeSet.

  18. Input: Set of decision sequences Knowledge Base Construction Algorithm Output: Hypertree of knowledge base H_tree Step 1:Initialize H_tree=Ø Step 2: For each decision sequence DSmwhere m = 1, 2,...,S For each decision in decision sequence DSmdo: Goto Step 3 Endfor Endfor Return H_Tree Step 3: For each entity ei in {e1, …, en} do: Ifei is not in H_Treethen add new node eito H_tree Endfor IfA is not in H_Treethen add new node A to H_tree Add new nodeSet to H_tree, with references to resulting node {A} and entity nodes {e1, …, en}and attribute value {a}.

  19. Hypertree Build based on two decision sequences Example – Constructing Hypertree

  20. Example –KBC (Step 1) First decision of the blue decision sequence

  21. Example – KBC (Step 2) Second decision of blue decision sequence

  22. Example – KBC (Step 3) Final decision of blue decision sequence

  23. Example – KBC (Step 4) First decision of the green decision sequence

  24. Example – KBC (Step 5) Second decision of green decision sequence

  25. Example – KBC (Step 6) Final decision of the green decision sequence

  26. Example – Constructing Hypertree Constructing Knowledge Base – Complete

  27. Best Decision Sequence where: • BDS is Best Decision Sequence, • N is the number of nodeSets for the entity node A, • NodeSet nsiis the i-th nodeSet of node A, • ai is the attribute value of nodeSet nsi , • {ej} is the j-th entity node in nodeSet nsi resulting to A, and • m is the number of decisions sequences that result to A by nodeSet nsi.

  28. Algorithm 2- Construction of Best Decision Sequences for List of Outcomes Input: Set of decisions to examine: Output: Best solution sequence for each final outcome in Out_set Step 1: Initialize array of solution paths Des_array[1,..,m] = Ø Step 2: Calculate BDS for all the solution sequences For each outcome sequence where n = 1, 2,...,m Calculate and store End For Step 3: Return Des_array[1,..,m]

  29. Algorithm 3- Best Decision Sequence Input: Node A Output: Best Decision Sequence Step 1:IfBDS(A) has already been calculated then return BDS(A) Step 2: Initialize: current_value = +∞ branch_value = +∞ Step 3:for each NodeSet of node A do Set branch_value = {a} + branch_value = min(current_value, branch_value)Record index k, from {ek} which minimized current_value Endfor Step 4: Return BDS(A)

  30. Example – Illustration of Algorithm 2 & 3 • Given the Hypertree from the previous example extended with new Decision sequence: i6, i7 Solution 3 (14) • Construction of Best Decision Sequences (Algorithm 2) is called with {Solution 1, Solution 2, Solution 3} on its input

  31. Example – BDS Solution 3

  32. Example – BDS Solution 1

  33. Example – BDSSolution 1 (Step 1)

  34. Example – BDSSolution 1 (Step 2)

  35. Example – BDSSolution 1 (Step 3)

  36. Example – BDSSolution 1 (Final)

  37. Example – BDSSolution 2

  38. Example – BDSSolution 2 (Step 1)

  39. Example – BDSSolution 2 (Step 2)

  40. Example – BDSSolution 2 (Step 3)

  41. Example – BDSSolution 2 (Step 4)

  42. Example – BDSSolution 2 (Final)

  43. Applications and Examples

  44. Medical Application • Input - Various sequences of tests prescribed by doctors for patients with similar symptoms and their final diagnoses • Goal - Identify the best sequence of tests for developing accurate diagnosis of patients with certain symptoms. Sequence is based on specified criteria. • Criteria - Cost, accuracy, lead time to diagnose

  45. Product Assembly Application • Input - Various feasible assembly sequences are captured in a knowledge base. • Goal – Construct assembly sequence based on sequences given at the input optimal with respect to certain criteria. • Criteria – Assembly time, cost, number of setups

  46. Assisting Drivers Finding Best Driving Directions Application • Input - various driving routes between the origin and destination. • Goal - examine those routes and produce a new path that is optimal with respect to specific criteria • Criteria - time, distance traveled, cost of gas

  47. Medical Application • Study involves medical history of group of patients with the similar initial symptoms and complains. • It is possible that during the examination of the patient several doctors prescribe different tests but arrive to same conclusion.

  48. Medical Application • Consider the diagnostic history of patients with similar symptoms. The history of symptoms and diagnoses are documented for the patients and later the patients are divided by a medical professional to groups of people who had similar symptoms. • During the examination of a patient from a group, every test prescribed by the doctor is recorded, doctor also supplies the system all the possible conclusions or intermediate decisions he/she makes based on results of each test.

  49. Medical Application • Operation is performing the test • Initial entities in the knowledge base model are medical tests • Intermediate entities are the decisions of doctors based on initial tests • Final entities are final diagnoses.

  50. Medical Application Construction of knowledge base for tests prescribed for patients with similar symptoms

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