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--- public:t720-atai-2012:methodology [2012/09/10 14:43] – [Methodology: How Do We Build an Intelligent Machine?] thorisson
+++ public:t720-atai-2012:methodology [2024/04/29 13:33] (current) – external edit 127.0.0.1
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 A reasonable hypothesis would be that present methodologies in software engineering, having enabled us to build some of the most advanced and complex systems ever created in the history of mankind, might prove sufficient to realize AGIs. It certainly has been sufficient for making progress in building intelligent systems, and while the ones built so far are clearly not AGIs, they are vastly more capable than prior solutions in handling complex tasks, dealing with missing data, and doing tasks that only humans could solve before.
-There are not very many methodologies that have been created in the field of A.I. specifically targeted to build complete AI systems, but a few can be listed. For example, production systems were proposed as a formalism that would enable the creation of general intelligence. Being frustrated with the shortcomings of this approach for building reliable robots, the "subsumption architecture" was proposed, based on augmented finite state machines. An attempt at synthesizing all the best from prior ones is the Constructionist Design Methodology. More recently the "belief, desire, intentions" methodology has been increasingly appearing in the literature. All of these stand firmly on prior and present software design methodologies, and while all of them add ideas specific to A.I. development, they are more or less confined to what can be done with current programming languages on current network designs. Therefore they are also subject to the same restrictions as these methodologies. This makes the task of evaluating their potential for creating AGIs a bit easier than otherwise, because the pros and cons of these methodologies are reasonably well documented.
+There are not very many methodologies that have been created in the field of A.I. specifically targeted to build complete AI systems, but a few can be listed. For example, production systems were proposed as a formalism that would enable the creation of general intelligence. Another early idea was blackboard architectures, which presented an architectural principle for building systems with heterogeneous components. Being frustrated with the shortcomings of this approach for building reliable robots, the "subsumption architecture" was proposed, based around augmented finite state machines. An attempt at synthesizing all the best from prior ones is the Constructionist Design Methodology. More recently the "belief, desire, intentions" (BDI) methodology has made increasing appearances in the literature. All of these proposals are firmly rooted in traditional software design methodologies, and while all of them add ideas specific to A.I. development, they are more or less confined to what can be done with current programming languages on current network designs. Therefore they are also subject to the same restrictions as these methodologies. This makes the task of evaluating their potential for creating AGIs a bit easier than otherwise, because the pros and cons of these methodologies are reasonably well documented.
 Production systems are based around productions rules with a pattern {P1, P2}, where P1 is an IF part and P2 is a THEN part, so that if P1 matches a particular piece of data it "fires" -- P2 is executed (or put on a list for potential subsequent execution). The system matches P1 to data in working memory, if there is a match the production is put in the "fire" list. For a single cycle, when relevant productions have been matched and some of which may match some data in working memory, the matching productions are executed. Thus for any problem there will be a series of matching and executing production rules essentially implementing a search in "solution space" for the solution to a particular goal. When solution to a problem -- a path to a goal -- has been found, the path can be "compressed" into a more compact format that dismisses the historical facts of the search that was originally necessary to find it. Key important aspects of such systems are (a) the design of the production rules -- their form and expressive power; (b) the control of the matching process (computational efficiency can be greatly impacted depending on the method chosen); (c ) the handling of conflicting production rules; (d) the parallelization of the matching and execution process.