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

2. 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

3. 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

4. … 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

5. 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” …

6. 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)

7. 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

8. 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?

9. 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)

10. 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.

11. 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

12. 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?

13. 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 –
Eliza
Dennett, the "Intentional Stance" and instrumentalism
Idea that notions like “intelligence” are attributed
Linked to anti-essentialism and anti-realism

14. 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)

15. 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

16. 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?

17. 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