1. Department of Computer Science© G.M.P O'Hare University College Dublin DEPARTMENT OF COMPUTER SCIENCE Multi-Agent Systems(MAS) & Distributed Artificial Intelligence(DAI) Lectures 3&4

2. Department of Computer Science© G.M.P O'Hare The Structure of an IKBS I An intelligent Knowledge Based System (IKBS) can generally be thought of as consisting of three subordinate parts. While the structure of an IKBS has become all too well established the terminology has not. John Fox describes the three parts as: “the triptych of data, the knowledge base and the host program”. Nilsson, however, considers the IKBS to be comprised of  a global database,  a set of operators on the database and  a control system for deciding when to apply such operators.

3. Department of Computer Science© G.M.P O'Hare The Structure of an IKBS II Michie describes the same structure as: “... corpus of knowledge and a comparatively simple mechanism for applying the knowledge in an opportunistic way to solve the problem”. The terminology I shall adopt is similar to that commissioned by Davis & King in “An Overview of Production Systems”. The three components being those of: rulebase  a rulebase, database  database and inference engine  an inference engine.

4. Department of Computer Science© G.M.P O'Hare The Structure of an IKBS III While these may not always be distinct, functionally they are certainly accounted for. To quote Fox:“they are essential in the same sense that a reference signal a comparator and a feedback loop are essential to a control system.”

5. Department of Computer Science© G.M.P O'Hare The Rulebase I The Rulebase consists of a set of production rules. The Production rule is the mechanism which is generally employed to represent the domain experts knowledge. In their simplest form each rule consists of: antecedent a left hand side often referred to as the antecedent and consequent a right hand side often referred to as the consequent. The ruleset will have a predetermined ordering which will be utilised by the interpreter.

6. Department of Computer Science© G.M.P O'Hare The Rulebase II Let us look at a typical rule: ANTECEDENT >----------X----------> CONSEQUENT HOT AND SUNNY >---------0.8---------> GOOD DAY The rule can be interpreted as a simple if... then construct. Thus: IF it is hot and sunny THEN we can conclude that it is a good day, with a degree of certainty of 0.8.  Rules will vary in format depending on the actual system.  Rules may or may not have an associated certainty factor.  Some systems impose constraints on the form of the antecedent & the consequent.  Specific systems permit only a single clause on the left hand side whilst others only allow a single consequent.

7. Department of Computer Science© G.M.P O'Hare The Rulebase III Furthermore the same rule may be interpreted in completely different manners by two different inference engines. One inference engine could interpret our aforementioned rule as follows... IF Hot and Sunny THEN add Good Day to database while another could understand it as... IF Hot and Sunny THEN replace Hot and Sunny in database by term Good Day.

8. Department of Computer Science© G.M.P O'Hare The Database I The database at any given instance contains a set of symbols that represent or reflect the state of the world. The database is dynamic. A change in the contents of the database corresponds to a change in the world the expert system models. As with the rulebase, the interpretation of the database is inference engine dependent. As Davis and King suggest if the IKBS were being used to explore symbol processing aspects of human cognition then the contents of the database would be understood to represent the contents say of the short term memory.

9. Department of Computer Science© G.M.P O'Hare The Database II When the application is that of a knowledge based expert the database is assumed to contain facts and assertions about the domain in question. Such a database would have no restrictions on its size or complexity unlike the former which would have an upper limit of seven or nine items. The databases can be thought of as the sole storage medium for an IKBS. This concept is referred to as the:'unity of data and control store’.

10. Department of Computer Science© G.M.P O'Hare The Database III Every part of the database is accessible by every rule in the rulebase. Nilsson emphases the fact that the database acts as a communications channel. Rules cannot communicate directly with each other, but rather only indirectly via the contents of the database. Strong analogies exist here with the monitor construct of Pascal Plus. Rules can be thought as sharing the contents of the database. Only one rule may access database at a particular instance hence mutual exclusion.

11. Department of Computer Science© G.M.P O'Hare The Inference Engine I Interpreter Sometimes referred to as the Interpreter. It identifies the set of rules which may be applied or ‘triggered’ at a particular instance, it then subsequently selects one such rule and executes it. In its simplest form it can be regarded as a select execute loop. Each time a rule is executed the contents of the database changes. Consequently, the next time the select-execute loop is entered a complete re-evaluation of the rulebase is performed, every rule being inspected by the inference engine as a potential contender for execution.

12. Department of Computer Science© G.M.P O'Hare Inference Engine II In the past researchers suffered from the misconception that the power of an IKBS lay in the inference engine. power strategy Minsky and Papert refer to this as the power strategy. More recently this has been refuted. Fox indicates that it is now widely accepted that quite unsophisticated algorithms are sufficient to provide the necessary control. Feigenbaum in agreement states “...power exhibited... is primarily a consequence of the specialist knowledge employed by the agent and only very secondarily related to... the power of the inference method.” He goes on to say: “Our agents must be knowledge rich, even if they are methods poor.”

13. Department of Computer Science© G.M.P O'Hare The Operation of an IKBS I Assume the Rulebase contains the following rules: 1 ATTRACTIVE AND GOOD --> ELIGIBLE PERSONALITY 2 MALE AND BUTCH --> ATTRACTIVE 3 FEMALE AND PRETTY --> ATTRACTIVE 4 TALL AND STRONG AND NOT THIN --> BUTCH AND NOT FEMININE 5 SMALL AND FEMININE --> PRETTY 6 FEMALE AND RESERVED --> FEMININE 7 FUNNY OR WITTY --> GOOD PERSONALITY

14. Department of Computer Science© G.M.P O'Hare The Operation of an IKBS II and that the database initially contains the following facts: FUNNY FEMALE RESERVED SMALL What can we deduce?

15. Department of Computer Science© G.M.P O'Hare The Operation of an IKBS III Assume that the Inference Engine...  RULE ORDER CONFLICT RESOLUTION STRATEGY  RE EVALUATION FROM RULE 1 Notice that when discovering what we can deduce from the limited knowledge we have at various points several rules may be triggered. This set of rules are referred to as the ‘conflict set’. The inference engine chooses which of the set to actually fire. The mechanism used varies from inference engine to inference engine. Indeed an interpreter may have several techniques to choose from. The strategy employed is called the conflict resolution strategy. rule order This particular IKBS employs a rule order conflict resolution strategy. Consequently that rule with the lowest rule number is fired.

16. Department of Computer Science© G.M.P O'Hare The Operation of an IKBS IV In the first pass through the rulebase rule 6 will be activated adding FEMININE to the database. Rule 6 is marked indicating that it should not be reevaluated. Because a change has occurred the entire rulebase needs to be reevaluated. Where should such reevaluation commence from? In this case rule 1 others may have selected rule 7. Rather than reevaluate every rule (except 6) an optimisation is sometimes employed, namely only reevaluate rules effected by the change in the database, rules 4 & 5. Would this cause problems? Rule 7 would never get fired. The optimisation can thus be employed with the proviso that if the effected rules can not be triggered then consider any unmarked rule.

17. Department of Computer Science© G.M.P O'Hare The Operation of an IKBS V Other more sophisticated conflict resolution strategies exist. To name but a few Generality order  Generality order, most specific rule applied. Recency order  Recency order, most recently executed rule applied. Cost order  Cost order, least computationally rule applied.

18. Department of Computer Science© G.M.P O'Hare Forward Reasoning Take the known facts and try to match against the LHS of a rule. This technique is commissioned when trying to discover what we can deduce. Sometimes called forward chaining.

19. Department of Computer Science© G.M.P O'Hare Backward Reasoning Take the goal state and decide what needs to be true for it to hold. We employ this technique when trying to decide if eligible. This method is often goal directed inference or backward chaining. Attempt to design an IKBS which will be able to decide if a shape is a triangle and if so more specifically what type? The database may initially contain knowledge like: 3 SIDES 2 ANGLES EQUAL

20. Department of Computer Science© G.M.P O'Hare The Knowledge Life Cycle I In a similar vein as the software life cycle we can identify an analogous knowledge life cycle. The first state involves discovering whether the particular problem in hand is amenable to solution via an IKSB approach. Should An IKBS Approach Be Used? A large number of problems may not require a computerised solution. Furthermore numerous problems may be solvable more readily via a conventional approach as opposed to an Expert System approach. According to Waterman an expert system approach should only be considered if: “expert system development is possible, justified and appropriate”.

21. Department of Computer Science© G.M.P O'Hare The Knowledge Life Cycle II The development of an IKBS system is regarded as being possible if the problem exhibits the following attributes.  The problem requires merely cognitive skills.  No requirement for physical skills or manual dexterity.  The problem does not require common sense, AI systems are inappropriate where the application relies heavily upon common sense reasoning.  The problem must be well understood, if the problem is poorly understood then there is little likelihood of a solution being obtained.

22. Department of Computer Science© G.M.P O'Hare The Knowledge Life Cycle III The application should not be too complex, the larger the application domain the greater degree of knowledge required (appears exponential) system less likely to demonstrate an acceptable level of performance. Experts must exist so that the knowledge can be extracted from them and subsequently encoded in the expert system. Consensus among experts regarding solution. Expert must be able to articulate techniques.

23. Department of Computer Science© G.M.P O'Hare The Knowledge Life Cycle IV The fact, however, that an IKBS solution is possible is not in itself sufficient. Possible justifications are as follows: Economic  Economic, saves money manpower, reduces maintenance costs. Skill shortage  Skill shortage, skill needs to be preserved due to scarcity or loss of staff. Skill distribution  Skill distribution, skills can be employed at numerous sites. In general an IKBS approach seems appropriate when the problem is non trivial, yet of manageable size, and requires the availability of symbol manipulation and heuristic problem solving techniques.

24. Department of Computer Science© G.M.P O'Hare The Knowledge Life Cycle V The knowledge life cycle consists of several identifiable discrete stages. According to Buchannon it consists of Identification  Identification, what important aspects problem. Conceptualisation  Conceptualisation, what concepts required to produce solution. Formalisation  Formalisation, what knowledge required. Implementation  Implementation, how to represent this knowledge. Testing  Testing, how can quality of knowledge be tested.

25. Department of Computer Science© G.M.P O'Hare The Knowledge Life Cycle VI I tend to regard the knowledge life cycle as consisting of: 1 ESTABLISHING PROBLEM SUITABILITY. 2 KNOWLEDGE ACQUISITION 2 KNOWLEDGE ACQUISITION 3 KNOWLEDGE REPRESENTATION 3 KNOWLEDGE REPRESENTATION 4 TESTING 4 TESTING 5 UTILISATION 5 UTILISATION 6 EVALUATION 6 EVALUATION 7 DEATH 7 DEATH