Knowledge Representation

1. Knowledge Representation • rule-based systems: those that use the knowledge structure if premise(s) then conclusion(s) • forward and backward chaining are the common inference strategies • logical theorem proving is the general inference paradigm - when KB is encoded as logical rules using AND, OR, NOT, implication, derive the logical truth or falsehood of some expression using the KB as a theory - theorem proving is an active area in AI - theoretical result: not all deductions are decideable; thus inference is a fundamentally complex and undecideable problem • uncertainty also used • Prolog: form conclusion :- premise, premise, ... conclusion.

2. Knowledge Representation Problems with rule-based systems • multiple evaluation of rule (hence saving of user input) • large rule sets: inefficient, maintainability - 50 rule modules maximum - if problem decomposition is difficult, perhaps change the knowledge representation (eg. add frames) • uncertainty: choosing a technique, interpreting results • misfit of problems to rule-based paradigm - not all problems naturally suited • procedural code in rules - forcing procedural execution (eg. Oops) can be messy - Prolog hooks are a good solution

3. Other techniques i) Frames and object-oriented programming ii) multiple contexts and truth-maintenance systems iii) model-based representations iv) blackboards v) Case-based reasoning (CBR) Rule of thumb: When a knowledge base is difficult to understand and use, consider changing the representation. Main goal: close the semantic gap between expert’s knowledge and computer implementation

4. (i) Frames (review) • knowledge structuring technique • "frames": lmowledge representation exploiting structure & inheritence "object-oriented programming": associating all data and computation with objects (= frames); computation done via message passing • benefits: - aids structuring of KB - more generic rules, therefore reduce size, complexity of KB - puts data in one area/module (a frame declaration) - maintainability of KB - objects, once identified, can contain characteristics, rather than spread this knowledge throughout many rules - inference engine can more effectively use/modify the data

5. Frames (cont) • To use frames during knowledge engineering i) decide on type of reasoning (forward, backward, other...) ii) decide on tasks for rules and processing requirements -iii) sketch a few rules, design a small hierarchy of frames, and test • AI workstations have graphical frame interfaces - permits quick definition, debugging Problems with frames • learning period for proper use and application • efficiency: can be lots of execution time overhead - eg. one object change can entail 100's of subsidiary commands • mis-use: create an inappropriate frame hierarchy/taxonomy - knowledge becomes difficult to understand - can also use too much procedural code, when declarative would be preferred

6. Frames p.250

7. (ii) Multiple contexts • context: single universe/world in which inference pertains • some problems have different environments which result in different solutions eg. time, season, path/direction, geographic location, .... • multiple context expert system: performs separate inferences for different contexts, using the same ruleset - otherwise, one inference and rule set to do the same - complicated KB and rules, that need to reconcile different contexts - inefficient: too much inferencing and backtracking • one can have multiple "parallel" inferences for all the contexts; then one can inspect the results for each and determine which is best alternative - each uses identical KB rules, but may require their own environments (variable binding records, run-time databases, ....) • akin to parallel processing instead of backtracking

8. Multiple contexts p.256

9. Multiple contexts p.257

10. Multiple contexts • Applying multiple contexts 1. selecting context structure for problem - problem specific - often hierarchical structure are well-suited 2. Reasoning with contexts: "fit" of context to problem <---> reasoning (i) parallel reasoning: mutually exclusive sub-problems (ii) pruning: trim computation space (like a "cut") - else resources can be overwhelmed - dominance: once one context is known to be best, then kill others (iii) merging: when 2 contexts have identical facts and assumptions, incorporate them together

11. Truth maintenance systems • monotonic reasoning: set of beliefs grow as inference proceeds non-monotonic reasoning: adding contexts can reduce belief set (eg. by retracting facts) • Truth maintenance systems (TMS): (consistency mgmt. systems) - when one assumption changes, all conclusions from that assumption must be retracted (repeated) • Three types of TMS: i) Justification TMS: simplest - set of beliefs in assumption set is monotonic, never shrinks - facts are either believed or not; but not believing a fact is not the same as believing it is false ii) Logic-based TMS - each predicate is true, false, unknown; represented in logic - contradictions are detected efficiently iii) Assumption-based TMS - conexts used in parallel (as in previous overheads)

12. TMS p.266

13. TMS p.267

14. Multiple contexts • Searching multiple contexts: a tree of all possible inferences (i) breadth-first: each level is generated one-by-one, fairly - complete, but resource expensive (ii) depth-first: Prolog technique - efficient, but possibly non-terminating (iii) controlled: more arbitrary, can move about tree as required "general theorem proving"

15. (iii) Model-based representation • encode knowledge in a structure which naturally describes it • use: realtime monitoring and diagnosis of machinery - can reason about common and uncommon failures (i) Static models • graph, node & arcs • good for representing connection paths between components/modules/... supply input: forward chaiing supply output: backward chaining • benefits: - clarity: can permit visual representation - a direct simulation of object of interest - familiarity: permits better debugging of KB - maintenance: change components in model, rather than KB rules - simplification: KB is simpler - efficiency: compact representation of domain, smaller than with rules/facts

16. Static models p.289

17. Static models p.291

18. Model-based representation (ii) dynamic models: • the state of the model changes over time, and this state change is the phenomemon of interest • simulation language/systems • supply inputs, system generates output, which is simulated dynamic behavior of whole system • used when actual domain unavailable, therefore a formal model is used as a substitute eg. nuclear power plant, earth's ozone layer, aircraft modelling, ... • used in many AI systems (eg. KEE) • benefit: can ascertain data that would normally have to be treated as UNKNOWN

19. (iv) Blackboards • blackboard: representation of knowledge with a high-level controlling element • first used in Hearsay II • components: knowledge sources (KS) : modules with specific expertise (tests, actions) blackboard: knowledge storage and communication control: overall problem-solving strategy • some control strategies - event-driven: react to events - expectation driven: predict solution based on current BB state, and act appropriately - request driven: control given to the KS which seems appropriate to a particular request - goal directed: control given to KS likely to solve some goal

20. Blackboards p.304

21. Blackboards • Some conditions for BB use • static knowledge sources: KS's don't change structure (BB does) • decomposable knowledge: get a small number of complex KS's • independence of KS's: run independently without hooks to one another • - BB is a medium that buffers them • hierarchical problems: layer problem domain • - each level taken care of by one or more KS's

22. Blackboard characteristics • Diverse approaches to problem solving • modular, independent • Common language for interaction • formal interfaces • Flexible representation of information • Efficient storage and retrieval of information • permits KS’s to inspect BB, and retrieve info as necessary • Organized participation • a distributed computing problem • Iterative problem solving • KS’s contribute to solution incrementally

23. Blackboards • Some conditions for BB use • static knowledge sources: KS's don't change structure (BB does) • decomposable knowledge: get a small number of complex KS's • independence of KS's: run independently without hooks to one another • BB is a medium that buffers them • hierarchical problems: layer problem domain • each level taken care of by one or more KS's

24. Blackboards p.315

25. Blackboards When to use BB's • real-time processing (speach, signal processing) • some scheduling and planning applications • problems which naturally decompose into separate independent tasks • problems which are best handled procedurally - this is also a pitfall, as BB and procedures can be applied to problems for which declarative rule-based methods would be perfectly suited

26. (v) Case-based Reasoning (CBR) • Case: operational description of a past problem and its solution • CBR idea: experts use vast amount of past experiences to derive new solutions • their compiled knowledge often uses pattern matching on long-past experiences • CBR records and documents a history of past cases • then attempts to match a given program with closest past case • the more the CBR system is used, the larger it’s case history becomes, and the more effective it can be • ideal when a problem is difficult to formulate in terms of explicit rule-based knowledge • often, the closest case matching a new problem is sufficient for correctly diagnosing the problem • Issue: newer cases are usually more pertinent than older ones • CBR system should account for case ages • Note: inductive inference (ID3) is one style of CBR • each example in table is a case