Introduction to AI & AI Principles (Semester 1) WEEK 7 John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham, UK

REVIEW

A Taste of “Predicate Logic”  Predicate logic adds ability to deal also with entities, properties and relationships explicitly, as well as universal generalization (  ) and existential generalization (  ).  Some examples of predicate logic expressions: happy(TheodosiaKirkbride) taller-than(TheodosiaKirkbride, MaryPoppins) criticizes(TheodosiaKirkbride, MaryPoppins, 14feb05) happy(TheodosiaKirkbride)  sad(MaryPoppins) happy(TheodosiaKirkbride) )  sad(MaryPoppins)  x (is-person(x)  rich(x)   happy(x))  y (is-person(y)  rich(y)  sad(y)) uStandard predicate logic has no inbuilt facilities for other sorts of generalization or propositional structure.

NEW for Week 7

Predicate Logic—The Meat uPredicate logic itself just consists of special symbols such as:        ( )  and the syntax (grammar)—how to structure expressions …  and general rules about the semantics of expressions (their meanings) …  and general procedures for doing deductive inference.  The particular symbols for entities, properties and relationships (e.g., TheodosiaKirkbride, happy, taller-than ), and their meanings,  are up to the particular representation-developer.

Truth Values in Logic (Part of the Semantics)  Each formula (i.e., expression that makes a statement) is considered to be either TRUE (T) or FALSE (F).  This is the formula's truth value (sometimes called its valuation).  Formulas with propositional structure have their truth values determined rigorously by the truth values of their subformulas.  Related principles handle generalization.  There is no middle ground between TRUE and FALSE.  Clearly, this definiteness is a problem, in the case of many types of statement.

Representing States at Different Times  Standard logic has no inbuilt facility for changes of truth value because of changes in the represented world.  So, the formulas are either about matters that are unchanging by their very nature (“eternal” matters): 91 is prime  or are implicitly about some particular “time-slice” of the represented world: Mary has three cars  or are explicitly about some particular time-slice: On 22 Feb 2005 at 10am, Mary had three cars.

Changing One’s Mind  Equally, standard logic does not itself contain any mechanism for a system changing its mind about the truth of something, e.g changing its mind about whether 91 is a prime number or not, or about whether Mary has three cars at 10am on 22 Feb  However, in an AI system, formulas can be made to change in truth-value (either because of world change or system changing its mind, or both), if special, extra mechanisms are added.

Why Logic Has Been Proposed uDesire to capture human rationality. uDesire for general-purpose representation/reasoning approach. l General purpose in terms of both subject matter and role in cognition (info from vision, sentence meanings, internal memory, …)  Desire for common format for explaining what is going on in other representation/reasoning approaches.

Rationality uMuch concentration in history on people as (in part) rational beings. uRationality as involving sound reasoning (deduction) l i.e. reasoning where truth of outputs is guaranteed by truth of inputs. uAnalysis of deduction, leading to development of standard logics as (supposedly) important descriptors of human thought. uSpillover of the results, and some of the thinking behind it, into AI. uStandard logic does not have inbuilt facilities for unsound reasoning (involving uncertainty, assumptions, abduction, induction, analogy, …) even though this is crucial in real life … uIt’s quite difficult to add such facilities, but there have been many proposals.

“General-Purpose” Aim  Reaction to: completely ad hoc, special-purpose representations, and representation styles, created for specific tasks, specific types of task or specific types of information. Consequence of such representations: Duplication of representational design effort when approaching a new problem. Difficulty of learning transferrable lessons about representational design. Need for creating tailored reasoning methods to cope with the specialized representations.  A single AI system may need to deal with a wide variety of tasks and types of information, perhaps all mixed up together. Having disparate representation styles for different types of information causes problems

“General-Purpose” Aim: Caveats  But this doesn't mean that AI systems should not use specialized approaches at all, or that you can't have mixes of styles. Could well be a good idea. NB: human use of different representational styles for different things: natural language, specialized (e.g., technical) forms of natural language, mathematical notation, diagrams, pictures, musical notation,..., and we're quite used to mixing these with each other … even mixing different natural languages.  Logic has quite severe limitations as regards both representation and reasoning, and is more suited to some things than others. So, “general purpose” is merely an aspiration.

“Common Format” Aim  The variety of proposed special-purpose representations, and the complications in some of them,  make it convenient to have a relatively simple, relatively standard language into which to (theoretically) translate them, in order to see how well conceived they are compare their advantages and disadvantages find, possibly, a quicker route to developing meaning principles, reasoning schemes and mathematical results about them.  Having a standard representation/reasoning style eases communication between researchers.

Now for “Semantic Networks” and “Frames”: see Bullinaria’s slides for week 6 uApart from readings mentioned there, see pp in Callan.