1 / 42

Knowledge Representation

Knowledge Representation. IIS2011015 Bakshi Rohit Prasad IIS2011016 Mukesh Kumar. “In the knowledge lies the Power”. Outline. What is Knowledge? Need to represent Knowledge What is Knowledge representation Storage of Knowledge

hoshi
Download Presentation

Knowledge Representation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Knowledge Representation IIS2011015 Bakshi Rohit Prasad IIS2011016 Mukesh Kumar “In the knowledge lies the Power”

  2. Outline • What is Knowledge? • Need to represent Knowledge • What is Knowledge representation • Storage of Knowledge • Principles of Knowledge Representation, Entities & Relational Structures • Nested Hierarchical Knowledge Organization • Definitions of the Principles of Knowledge Representation • Types of Entities • Properties of Labels • Multiresolutional Character of Knowledge & its Complexity • Recursive Algorithm for constructing Multi-scale KR • Virtual Phenomenon of Knowledge Representation • References

  3. What is knowledge? essentially any meaningful & coherent expression that can be represented. May be true or false Belief A justified belief that is not known to be true. Hypothesis • Where does knowledge comes from and how? Experience & understanding • Experience : record of an event or a group of an event • Initial State • Final State • Actions • Evaluation of the cost of changes(losses) and the gain attended as a result of these changes. • The Final Goal True Justified Belief Knowledge Knowledge should not be confused with data. Knowledge includes and requires the use of data and information.

  4. What is knowledge? Contd.. • Formally ”Knowledge is a data structure supporting the purposely organized process of operations together with a subset of operational techniques for decision making and organizing the information .” • Some related terms: • Epistemology: “Branch of Philosophy” study of knowledge, its nature origin, foundation, limits and validity. • Metaknowledge: Knowledge about knowledge

  5. Types of Knowledge • Declarative: passive knowledge expressed as statements of facts about the world. (Personal data in a database) • Procedural: compiled knowledge related to the performance of some task. (Steps used to solve algebraic eq.) • Heuristic: Knowledge used to make good judgments', strategies, tricks, or rules of thumb’ used to simplify the solution of problem. • Heuristics are usually acquired with much experience. Example: TV technician • Heuristic may not always correct but frequent, then lead to a quick solution.

  6. Comparative Study: Storage of knowledge In Biological organism knowledge is likely stored as a complex structure of interconnected neurons. Average human brain weighs about 3.3 pounds and contains an estimated no. of 1012 neurons with 1014 bits of potential storage capacity. In Computers, In the form of collections of magnetic spots and voltage states. 1012 its with capacities doubling about every three years.

  7. Storing the Knowledge contd.. Storing the knowledge should be predicated by its subsequent use. Updating of knowledge Efficiently Storing Knowledge Knowledge Enhancement Knowledge Retrieval Knowledge Compression via encoding or generalization Building up the multiresolutionalarchitecture

  8. Storing the Knowledge contd.. • Activities to be performed by subsystem of KR • Generating rules for sensory information interpretation(images, messages etc.) • Generating rules for task decomposition • Generating rules for job assignment • Generating rules for scheduling • Generating of error compensation • Anticipating states (Predicting) • Simulating plans developed by a subsystem of behavior generation.

  9. What is Knowledge representation? • It is a mechanism within which a model, or a set of models can emerge corresponding to (representing) the external system of interest. • This model, or a set of models can be used for solving problems for the system.

  10. Role of Knowledge representation Intellect: This property of the systems is associated with their ability to solve problems, reason and plan. Situation (State) Awareness: Any system can be said to be aware of its situation (state) when there exists a close correspondence between knowledge in the system's world model and the situation in the external world. Consciousness: The situation awareness can be enhanced if the system's world model includes representation of the external world together with the representation of the inner states of the system Reflection ("reflexia“):Consciousness can be enhanced too, if in addition to the ability to represent itself within the external world, the system is capable of representing its representation in the external world.

  11. Knowledge Representation in the Brain • The information processing part of the brain is composed of neurons. • Each neuron is a tiny computer that receives inputs through synaptic receptor sites on dendrites and its cell body. • It computes an output that is distributed to the inputs of other neurons via an axon that may have multiple branches • Each branch terminates on a receptor site on a dendrite or cell body of another neuron.

  12. Knowledge Representation in the Brain

  13. Knowledge Representation in Machines Formalized symbolic logics Semantic Networks Production Rules Frames

  14. Formalized Symbolic Language • Logic is a formal method of reasoning. • ‘All employees of the AI-Software company are programmers.’ (¥x)(AI-SOFTWARE –CO-EMPLOYEE(x)PROGRAMMER()) If it is also known that Jim is an employee of AI Software company. AI-SOFTWARE -CO-EMPLOYEE(jim) One can draw the conclusion that Jim is a programmer. PROGRAMMER(jim)

  15. Syntax and Semantics of Logics • Syntax • How we can construct legal sentences in the logic • Which symbols we can use (English: letters, punctuation) • How we are allowed to write down those symbols • Semantics • How we interpret (read) sentences in the logic • i.e., what the meaning of a sentence is • Example: “All lecturers are six foot tall” • Perfectly valid sentence (syntax) • And we can understand the meaning (semantics) • This sentence happens to be false (there is a counterexample)

  16. Propositional Logic • Syntax • Propositions such as P meaning “it is wet” • Connectives: and, or, not, implies, equivalent • Brackets, T (true) and F (false) • Semantics • How to work out the truth of a sentence • Need to know how connectives affect truth • E.g., “P and Q” is true if and only if P is true and Q is true • “P implies Q” is true if P and Q are true or if P is false • Can draw up truth tables to work out the truth of statements

  17. First Order Predicate Logic • More expressive logic than propositional • Syntax allows • Constants, variables, predicates, functions and quantifiers • So, we say something is true for all objects (universal) • Or something is true for at least one object (existential) • Semantics • Working out the truth of statement • This can be done using rules of deduction

  18. Example Sentence X ((day_of_week(X, monday) day_of_week(X, weds)) eat(me, dinner))). (go_to(me, house_of(john) • In English: • “Every Monday and Wednesday I go to John’s house for dinner” • In first order predicate logic:

  19. Higher Order Predicate Logic • More expressive than first order predicate logic • Allows quantification over functions and predicates, as well as objects • For example • We can say that all our polynomials have a zero at 17: • f (f(17)=0).

  20. Other Logics • Fuzzy logic • Use probabilities, rather than truth values • Multi-valued logics • Assertions other than true and false allowed • E.g., “unknown” • Modal logics • Include beliefs about the world • Temporal logics • Incorporate considerations of time

  21. Why Logic is a Good Representation • Some of many reasons are: • It’s fairly easy to do the translation when possible • There are whole tracts of mathematics devoted to it • It enables us to do logical reasoning • Programming languages have grown out of logics • Prolog uses logic programs (a subset of predicate logic)

  22. Principles of Knowledge Representation, Entities & Relational Structures • Some of the common principles followed by the many architectures of KR are : • Principle of multiple symbol labeling system. • Causality Principal. • Principle of Continuity. • Principle of Efficiency. • Principle of limited resource. • Principal of Thesaural Interpretation of Words. • Principle of Entity-Relationship KR. • Principle of Incompleteness. • Principle of Multiresolution Representation • Feed-forward Principle.(Goal-oriented decision making) • Principle of Feedback.(Compensation-oriented decision making) • Principle of Creativity.(Combinatorial techniques of decision making)

  23. Nested Hierarchical Knowledge Organization

  24. Nested Hierarchical Knowledge Organization The behavior of the systems form hierarchies. Knowledge representation for the Phenomenon and sub-phenomenon (processes, behaviors, controls, knowledge of events and sub-events) should be done in such a way that: Reflects the 12 principles of knowledge organization

  25. Nested Hierarchical Knowledge Organization : Properties • Computational Independence of the resolution levels : Each level describe the same control process with different levels of accuracy and different time scale. • Representations of different domains of the overall system process resides at a level of resolution : All loops are performing the same operation at different levels of resolution but they are dealing with different subsets of the world. • Correspondence among the different levels of resolution & different frequency bands within the overall process : The resolution of the level is associated with the frequency of sampling. eg. Cameras Resolution.

  26. Nested Hierarchical Knowledge Organization : Properties Capability of loops at different levels of resolution to integrate into the ELF diagrams. Correspondence between the upper and lower parts of the ELF diagrams. Formation of behavior of the system as a superposition of behaviors generated by the actions at each resolution level. Similarities between the algorithms of behavior generation at all levels. Evolution of hierarchy of representation from linguistic to analytical at the bottom. Eg. Robot.

  27. Definitions of the Principles of KR • Principle of Thesaural Interpretation of words. • Thesaurus – A thesaurus is a global dictionary that contains interpretations for all words (Labels) and combination of words that can be expected in the system based upon its prior experiences. • Principle of Entity-Relational WBK Representation • Here entities and relations are both in the form of words. • What are entities? • What are Relations?

  28. Definitions of the Principles of KR • Principle of Multiresolutional Representation. • Infact in only one resolution all facts cannot be adequately represented. • Eg. Class of Animals( Carnivorous, Herbivorous) • Such structure is also used in general as – • Its is expected that some object will be found that requires greater level of resolution • Complexity of representation decreases.

  29. Definitions of the Principles of KR • Causality Principle • Cause (C) – Effect (E) representation in WBK representation. • Eg. Throwing stone on a mirror. • Principle of Heterogeneity • Any WBK representation is heterogeneous. A heterogeneous representation consists of more than one language. • The exact relationship between languages in a heterogeneous representation is not known.

  30. Definitions of the Principles of KR • Principle of Efficiency • Efficiency of a system includes : • Expressive power • Computability : It is the ability of a system to arrive at a complete and consistent solution in reasonable time • Complexity : Number of computations required to solve a problem • Principle of Incompleteness • Any knowledge representation is Incomplete.

  31. Definitions of the Principles of KR • Feed forward Principle. • Here emphasis is on the goal. We want to achieve a certain goal. • All decisions are then taken in order to achieve the goal. • No feedback of the situation is available. • Principle of Feedback. • Here new decision toward the achievement of goal depends on the current feedback from the environment. • Principle of Limited Resource. • Input to the system is restricted. • Limitation can be of view, time, money.

  32. Definitions of the Principles of KR • Principle of Continuity. • Representation of knowledge should be continuous. • Each snapshot of system should be continuous and consistent. • Principle of Creativity. • Creativity is embedded in the system if it is capable of discovering new knowledge from its surroundings. • Eg. Chess game in Fork situation.

  33. Types of Entities • External Entity • External entity is something that can be named and exists in the real world. • Internal Entity • Internal entity is a data structure defined in the intelligent system. • An internal entity may represent a real world object eg. a group of pixels representing a building. • An Internal entity may also represent something that has never been experienced in the real world eg. Daemons, spirits, angels.

  34. Properties of Labels • Labels are words or strings of words used together for representing some object or relation in a system. • Generation of classes • Suppose there are few different strings of labels which the intelligent system has received as input. • Eg. Induction Motor, Stepper Motor, DC Motor. • Grouping them is possible if we generate a class with label Motor. • Generalization • The operation of generalization is associated with transforming the world representation to a lower resolution level

  35. Multiresolutional Character of Knowledge & its Complexity • State space decomposition. • Let us consider an n-dimensional state space and a domain X having diameter d and volume V • This domain can be divided in a finite number of non-intersecting sub-domains. • Level • Threshold • Tessela : Elementary sub-domain. • Tesselated state space.

  36. Multiresolutional Character of Knowledge & its Complexity • Accuracy • More the number of elementary sub-domains (Tessela) greater the accuracy. • Cardinal Number H • Vav : The volume of the elementary sub-domain. • V : The volume of the whole domain space. • Cardinal Number is defined as : H = V / Vav • Greater the cardinal Number, greater the accuracy.

  37. Recursive Algorithm for constructing Multiscale KR • Step 1 : Get information at highest available level of resolution. • Investigate properties of uniformity (density of points). • Group elementary units and label them. • Check for the consistency of clusters. • Store the results as representation at lower resolution level. • If no new clusters goto step 2 else goto step 1. • Step 2 : Send representations of all resolution levels to the overall system of representation.

  38. Virtual Phenomenon of KR • Need of Representation. • Richness of immediate experiences. • Visual images are filled with color, motion, 3-D. • Auditory experience have a wide range of frequency, intensity, directional sense etc. • Representation is done in three ways: • Representation using immediate sensory processing. • Intermediate Representations. • Long term memory representations.

  39. Virtual Phenomenon of KR • Representation for immediate sensory processing. • Observed Signals. • Estimated Signals. • Predicted Signals. This representation only lasts for the moment till which the event takes place and there after it is destroyed. • Intermediate Representations. • This is also called short-term memory. • This is like RAM of computers where data persists till it is not overwritten. • Here representation is inform of symbols representing position, size, color, shape, motion of the captured object.

  40. Virtual Phenomenon of KR • Long-Term memory representations. • This acts as a repository of information storage where information can be stored for a life time. • Here information is stored solely in symbolic form. • It can also preserve the temporal ordering of information by use of strings, graphs or frames. • Generalizations can be done on the information stored here. • Once well in place, the information can exist for decades in long term memory representations.

  41. References • Intelligent Systems (Architecture, Design and Control) - Alexander M. Meystel, James S. Albus. • Artificial Intelligence and Expert System by D. W. Patterson • Artificial Intelligence by Rich & Knight • Web References… • www.wikipedia.com

  42. Thank You

More Related