《情报科学原理 Principles of Information Science（PIS）》课程PPT教学课件：Chapter 7 Information Regeneration - Decision Making Theory

Principles of Information Science Chapter 7 Information Regeneration Decision Making Theory

Principles of Information Science Chapter 7 Information Regeneration -- Decision Making Theory

List of contents From Knowledge to Strategy 2. Classical Model of Decision-Making 3. Information Theory of Decision-Making 4. Unified Theory

List of Contents 1. From Knowledge to Strategy 2. Classical Model of Decision-Making 3. Information Theory of Decision-Making 4. Unified Theory

From Knowledge to Strategy

From Knowledge to Strategy 1

Model of Information Regeneration Goal to be sought Information Knowledge RegenerationStrategy Information about the for solving problem Problem and under the given Environment environment

Model of Information Regeneration Information Knowledge Regeneration about the Problem and Environment Strategy Information for solving problem under the given environment Goal to be sought

Knowledge and strategy Knowledge: A set of descriptions about the states and the varying laws of the states of certain categories of objects Strategy: a sequence of well organized action orders for solving specific problems under certain environment and certain goal. Strategy can also be regarded as a special group of information indicating the specific procedure of problem solving

Knowledge and Strategy Knowledge: A set of descriptions about the states and the varying laws of the states of certain categories of objects. Strategy: A sequence of well organized action orders for solving specific problems under certain environment and certain goal. Strategy can also be regarded as a special group of information indicating the specific procedure of problem solving

Mechanism from Knowledge to Strategy Initial state New Goal N Operation of database State Match Knowledge Rule Rule Base B ase Sequence strategy E Control Distance Indication

Mechanism : from Knowledge to Strategy P Initial State of Database Operation New State Goal Match G Rule Base Knowledge Base Rule Sequence N Y Strategy Distance Indication Control E

Algorithm for Knowledge Activation 1. Given P(the Initial State of the Problem), E(the Knowledge and the rule bases and g(the final state of the problem) 2. Select the best rule from the rule base so that whose left side matches the Initial State while whose right side leads to such a new state whose distance to the goal is the minimum compared with other selections by using the Knowledge 3. Check the new State thus obtained. If its distance to the Goal is the smallest one but unequal to zero do the same thing from the New state as did in step 2 4, Otherwise, re-select a rule at step 2 5. Until the distance between the new state and the goal equal to zero, or sufficiently small, go loop from step 2 to 4. The sequence of the rule applications is the strategy sought

Algorithm for Knowledge Activation 1. Given P(the Initial State of the Problem), E(the Knowledge and the Rule Bases) and G(the Final State of the Problem) 2, Select the best Rule, from the Rule Base, so that whose left side matches the Initial State while whose right side leads to such a New State whose distance to the Goal is the minimum compared with other selections by using the Knowledge 3, Check the New State thus obtained. If its distance to the Goal is the smallest one but unequal to zero, do the same thing from the New State as did in step 2 4, Otherwise, re-select a rule at step 2 5, Until the distance between the New State and the Goal equal to zero, or sufficiently small, go loop from step 2 to 4. The sequence of the rule applications is the strategy sought

Classical Model of Decision-Making

Classical Model of Decision-Making 2

An Example: Umbrella Tricky Benefits table Weather Sunny raining Benefits p actions Carry a(1) (1,1) c(1,2) Not Carry (2,1) c(2,2) a

An Example: Umbrella Tricky Benefits Table Weather Benefits actions Sunny Raining Carry a(1) Not Carry a(2) p 1-p c(1,1) c(1,2) c(2,1) c(2,2)

Decision rule Calculate the average benefit for each action C(1)=pc(1,1)+(1-p)c(1,2) C(2)=pc(2,1)+(1p)c(2,2) Choose the action with bigger benefit as the strategy If c(1)>c(2)then a(1) is chosen Otherwise, a(2)is chosen

Decision Rule C(1) = p c(1,1) + (1-p) c(1,2) C(2) = p c(2,1) + (1-p) c(2,2) Calculate the average benefit for each action: Choose the action with bigger benefit as the strategy: If C(1) > C(2) then a(1) is chosen; Otherwise, a(2) is chosen