New Directions in Information Technology Lecture 5 John Lee AI and Automation New Directions in Information Technology Lecture 5 John Lee

Formality and formalisation Central issue in AI and automation (but also much else): How to go from informal world to formal system, derive some result, and then get back again without losing anything important What should be preserved? truth? meaning? Formal statement Calculation (Inference) Informal statement Reinterpretation Result

Basic logical principles Analysis of English argument proceeds via translation into logical form, application of rules, then translation back … Compare analysis of arithmetical calculation: Suppose 82 students get 175 pages of notes each … Form is: result = A x B = 82 x 175 … Calculation gives: result = 14,350 So we need (e.g.) to budget for 14,350 copies Formal statement Calculation (Inference) Informal statement Reinterpretation Result

… basic principles (continued) If the switch is down, (then) the light is on; the switch is down … <Informal> If P then Q; P … <(semi-) formal translation> P –> Q P Q <formal inference> … therefore Q So the light is on <reinterpretation> Formal statement Calculation (Inference) Informal statement Reinterpretation Result

Some more simple examples … If Fred likes cheese or Jim likes cake, (then) Mary will do the cooking. Jim likes cake. So …? (P ˚ Q) ® R; Q; …? If Fred likes rhubarb, Jim will stew some. Fred likes rhubarb and custard. So …? P ® Q; P ˙ R; …? (assuming: “Fred likes rhubarb and custard” = “Fred likes rhubarb and Fred likes custard” …

Turing's machine an abstract machine head and tape: head can read, erase, write symbols, and move tape one square left or right head is defined by a few rules e.g.: if the symbol below head is `1', erase it, write a `0', and move one square left input for problem is posed by writing it on the tape at start time output from the problem is on the tape at `halt' time given machine defines a mathematical function (set of pairs of input/output)

Simple example … Head I I I I I I I I I I I I I I an adding machine — two numbers in `tally notation' separated by blank machine finds blank, `moves 1s across blank' until finished infinite (or extendable) machines — can always add more tape Head I I I I I I I I I I I I I I

Universal machines a universal machine can mimic any other Turing machine mimicked machine is encoded as number on U-machine's tape, along with input for particular problem for mimicked machine U-machine can mimic the encoded machine solving the problem <emulation> Turing then proved that there are functions which U-machine can't compute notably the `halting problem' — will machine halt when computing a given function?

What is so important about Turing's machine? active head vs. passive memory: treating program as data hardware vs. software — distinguish abstract computation from physical implementation consider any range of alternative implementations establishes an abstract `informational' level for describing behaviour in fact, engineered computers are like Turing machines with random access memory (RAM) and vastly complicated heads called central processing units (CPUs) (these are technically “von Neumann” machines)

Automation of logical proof Sometimes proofs can be computable Even whole systems of proof Programming languages can be based on this E.g. Prolog A language based on theorem proving from FACTS and RULES factorial(1, 1). factorial(N, F):- M is N-1, factorial(M, FM), F is FM+N.

Representation of knowledge (Contrast with data … knowledge is richer and includes means of deriving consequences) Rule-based systems Cf Prolog: represent everything with facts and rules … … then derive consequences by proof. Assumes all knowledge can be captured this way As in traditional expert systems Case-based reasoning Suppose that systems of rules will be too complicated … Instead store cases that have worked in the past, and some rules for working out how to re-use these

Approaches to formal semantics Meaning as truth conditions What does the world have to be like for a sentence to be true? Provides semantics for simple systems like propositional or predicate calculus Can be elaborated for use with natural languages, e.g. Consider the world at other points in time Consider other possible worlds What can this approach not capture?

Representational theories of mind The Computational Metaphor: hard and soft AI Contrast between focus on representation and focus on behaviour What is "intelligence"? Is it what you can do or is it how you do it? The Turing Test The Loebner Prize – http://hps.elte.hu/~gk/Loebner/TT.html Eliza Dennett, the "Intentional Stance" and instrumentalism Idea that notions like “intelligence” are attributed Linked to anti-essentialism and anti-realism

Connectionist approaches and non-representationalism Connectionism, or “neural-net”-based theories Distributed processing No explicit locus of symbols or syntactic structures Emergence The sum of a system can be more than its parts Environmental embedding and situated action Lucy Suchman Compare philosophical approaches of, e.g. Heidegger (existentialist embedding) Wittgenstein (social embedding)

Two classic critiques Dreyfus – phenomenology & Heidegger – Winograd & Flores Fundamentalist anti-representationalism Strong AI is impossible in principle Searle – the Chinese Room More pragmatic argument Homunculus knows nothing, hence system cannot be a locus of understanding Extended as claim that no mere symbol-processing system could ever “understand” Claimed to be an “in-principle” argument

Practical considerations Various general application fields Expert systems Either rule-based or case-based Verification systems To prove e.g. properties of safety-critical software Language engineering – LSA – etc. Used e.g. to mark essays (!) Dialogue systems Increasingly multimodal: speech, gesture, etc. ITSs Will teachers be replaced by computers?

Design/architecture applications Representation of design knowledge (contrast with Schön!) Cf. Coyne et al. Knowledge-Based Design Systems CBR Building performance evaluation systems Standardisation and automation in construction Prescriptiveness