Path: utzoo!attcan!utgpu!jarvis.csri.toronto.edu!mailrus!cs.utexas.edu!samsung!ctrsol!emory!cambridge.apple.com!bloom-beacon!eru!luth!sunic!mcsun!ukc!edcastle!aipna!cam From: cam@aipna.ed.ac.uk (Chris Malcolm) Newsgroups: comp.ai Subject: Re: What is a Symbol System? Keywords: computation, symbol manipulation, syntax, formality Message-ID: <1656@aipna.ed.ac.uk> Date: 22 Nov 89 19:15:33 GMT References: <11640@phoenix.Princeton.EDU> <17189@netnews.upenn.edu> <11657@phoenix.Princeton.EDU> Reply-To: cam@aipna.ed.ac.uk (Chris Malcolm) Organization: Dept of AI, Edinburgh University, UK. Lines: 83 In article <11657@phoenix.Princeton.EDU> harnad@phoenix.Princeton.EDU (S. R. Harnad) writes: >Here is an easy example. I think it contains all the essentials: >We have two Rube Goldberg devices, both beginning with a string >you pull, and both ending with a hammer that smashes a piece of >china. Whenever you pull the string, the china gets smashed by the >hammer in both systems. The question is: Given that they can both be >described as conforming to the rule "If the string is pulled, smash the >china," is this rule explicitly represented in both systems? > >Let's look at them more closely: One turns out to be pure causal >throughput: The string is attached to the hammer, which is poised like >a lever. Pull the string and the hammer goes down. Bang! > > In the other >system the string actuates a transducer which sends a data bit to a >computer program capable of controlling a variety of devices all over the >country. [ .. and which activates an explicit representation of the >rule, which in turn causes the hammer blow.] In your original posting you (Stevan Harnad) said: So the mere fact that a behavior is "interpretable" as ruleful does not mean that it is really governed by a symbolic rule. Semantic interpretability must be coupled with explicit representation (2), syntactic manipulability (4), and systematicity (8) in order to be symbolic. There is a can of worms luring under that little word "coupled"! What I take it to mean is that this symbolic rule must cause the behaviour which we interpret as being governed by the rule we interpret the symbolic rule as meaning. Unravelled, that may seem stupendously tautologous, but meditation on the problems of symbol grounding can induce profound uncertainty about the status of supposedly rule-governed AI systems. One source of difficulty is the difference between the meaning of the symbolic rule to the system (as defined by its use of the rule) and the meaning we are tempted to ascribe to it because we recognise the meaning of the variable names, the logical structure, etc. Brian Smith's Knowledge Representation Hypothesis contains a nice expression of this problem of "coupling" interpretation and causal effect, in clauses a) and b) below. Any mechanically embodied intelligent process will be be comprised of structural ingredients that a) we as external observers naturally take to represent a propositional account of the knowledge that the overall process exhibits, and b) independent of such external semantical attribution, play a formal but causal and essential role in engendering the behaviour that manifests that knowledge. [Brian C. Smith, Prologue to "Reflection and Semantics in a Procedural Language" in "Readings in Knowledge Representation" eds Brachman & Levesque, Morgan Kaufmann, 1985.] It is not at all clear to me that finding a piece of source code in the controlling computer which reads IF STRING_PULLED THEN DROP_HAMMER is not just a conjuring trick where I am misled into equating the English language meaning of the rule with its function within the computer system [Drew McDermott, Artificial Intelligence meets Natural Stupidity, ACM SIGART Newsletter 57, April 1976]. In simple cases with a few rules and behaviour which can easily be exhaustively itemised we can satisfy ourselves that our interpretation of the rule does indeed equate with its causal role in the system. Where there are many rules, and the rule interpreter is complex (e.g. having a bucketful of ad-hoc conflict-resolution prioritising schemes designed to avoid "silly" behaviour which would otherwise result from the rules) then the equation is not so clear. The best we can say is that our interpretation is _similar_ to the function of the rule in the system. How reliably can we make this judgment of similarity? And how close must be the similarity to justify our labelling an example as an instance of behaviour governed by an explicit rule? Why should we bother with being able to interpret the system's "rule" as a rule meaningful to us? Perhaps we need a weaker category, where we identify the whole caboodle as a rule-based system, but don't necessarily need to be able to interpret the individual rules. But how can we do this weakening, without letting in such disturbingly ambiguous exemplars as neural nets? -- Chris Malcolm cam@uk.ac.ed.aipna 031 667 1011 x2550 Department of Artificial Intelligence, Edinburgh University 5 Forrest Hill, Edinburgh, EH1 2QL, UK