|
[I found this book to be extremely helpful at presenting the basic
concepts of Artificial Intelligence in a way that makes sense and allows easier understanding.]
For Haugeland, Artificial Intelligence represents problem
solving through the use of heuristics. The concept is analogous to the human act of thinking. The concept
bears the label Artificial because it is the appearance of intelligent behavior that is often sought
- there may be no actual understanding of what is being accomplished.
The field of cognitive science looks at the bigger picture of thinking in general, regardless
of the specific instance.
p.4-5"According
to the symbol manipulation theory, intelligence depends only on a system's organization and functioning as a symbol manipulator...
if Artificial Intelligence really has... much more to do with abstract principles of mental organization, then the distinctions
among AI, psychology, and even philosophy of mind seem to melt away. One can study those basic principles using tools and
techniques from computer science, or with the methods of experimental psychology, or in traditional philosophical terms -
but it's the same subject in each case... For [this] new 'unified' field, [experts] have coined the name cognitive science."
Knowing is more complicated than acquiring, and we should focus our efforts on
the knowledge part of the AI system first.
p.11"given
a system capable of knowing, how can we make it capable
of acquiring?... AI has discovered that knowledge itself
is extraordinarily complex and difficult to implement -
so much so that even the general structure of a system with
common sense is not clear. Accordingly, it's far from apparent
what a learning system needs to acquire; hence the project of acquiring some can't get off the ground [5].
In other words, Artificial Intelligence must start by trying
to understand knowledge (and skills and whatever else is
acquired) and then, on that basis, tackle learning... But it does not appear that learning is the most basic problem, let alone a shortcut or a natural starting point."
General principles guiding a search through the possibilities can accomplish a lot.
p.12"What
gets programmed directly is just a bunch of general information
and principles, not unlike what teachers instill in their
pupils. What happens after that, what the system does
with all this input, is not predictable by the designer
(or the teacher or anyone else.) The most striking current examples are chess machines that outplay their programmers, coming up with brilliant moves that the latter would never have found. Many people are amazed by this fact; but if you reflect that invention is often just a rearrangement (more or less dramatic)of previously available materials, then it shouldn't
seem so surprising."
A formal system is a self-contained world unto itself.
p.50"formal
systems are self-contained; the 'outside world' (anything not included in the current position) is strictly irrelevant. For
instance, it makes no difference to a chess game, as such, if the chess set is stolen property [perhaps it was purchased at
a pawn shop - JLJ] or if the building is on fire or if the fate of nations hangs on the outcome - the same moves are legal
in the same position, period."
Formal systems seem to be digital by nature.
p.57-58"Consider the difference between
accidentally messing up a chess game and a billiards game. Chess players with good memories could reconstruct the position
perfectly (basically because displacing the pieces by fractions of an inch wouldn't matter). A billiards position, by contrast,
can never be reconstructed perfectly... The digitalness of formal systems is profoundly relevant to Artificial Intelligence...
Formal systems are independent of the medium in which they are 'embodied'."
Heuristics play a major role in AI. Haugeland talks here about heuristics and chess.
p.83"Obviously the chooser [of the move
to play in a game of chess] needn't always find the best move; even the greatest champions don't play perfect chess. The goal,
rather, is a system that chooses relatively well most of the time. In other words, an infallible test for the better move
(i.e., an algorithm) is not really required; it would be enough to have a fairly reliable test, by which the machine could
usually eliminate the worst choices and settle on a pretty good one. Such fallible but 'fairly reliable' procedures are called
heuristics (in the AI literature)... There are many rules of thumb for better chess."
Formal systems can use tokens which are assigned meaning.
p.99-100"Formal systems can be interpreted;
their tokens can be assigned meanings and taken as symbols about the outside world. This may be less than shocking news by
now; but a century ago the development of interpreted formal systems was a major innovation, with revolutionary consequences
throughout logic and mathematics. Moreover, if Artificial Intelligence is right, the mind is a (special) interpreted formal
system - and the consequences will be even more revolutionary for psychology."
What is a computer?
p.106"A computer is an interpreted
automatic formal system - that is to say, a symbol-manipulating machine."
When we tell a machine to search for a solution, we must be specific
as to what the machine is searching for, and in what region we are actually searching. We might
stumble upon the solution much by chance, but it would be better to have specific heuristic rules to guide the search efforts
of the machine. The search efforts should be guided by knowledge, however, it might be costly to acquire that knowledge.
Interestingly, Haugeland defines the search for a solution a two-part task: the identification of
the object to be searched for, and the identification of the relevant search space. The machine
seems to be wasting time by looking for the wrong thing or looking in the wrong directions. We could define a term like focus
to describe the attempt to guide the machine (heuristically) to look for the correct objects, in paths that are promising
or interesting. Much of human intelligence comes from the ability to focus on information that is timely and relevant,
and to identify cues in the environment that indicate the promising directions for further search.
p.176-177"Artificial Intelligence... The proud parents were a prolific
team of three: Allen Newell, Cliff Shaw, and Herbert Simon... The essential difference between Newell, Shaw, and Simon (hereafter
NS&S) and earlier work in cybernetics and machine translation was their explicit focus on thinking.
More specifically, they conceived of intelligence as the ability to solve problems;
and they conceived of solving problems as finding solutions via heuristically guided search... it's easy enough to
cast ordinary activity, or even conversation, as a series of mental quests... Every search has two basic aspects:
its object (what is being looked for) and its scope (the region or set of things within which the object
is sought). For actual system design, each aspect must be made explicit, in terms of specific structures and procedures. For
instance, a system cannot seek an object that it couldn't recognize: it has to be able to 'tell' when it reaches its goal.
Consequently, the design must include a practical (executable) test for success, and that test then effectively defines
what the system is really seeking.
The designer must also invent some procedure for working through the relevant
search space more or less efficiently... More generally, any well-designed searcher needs a practical generator
that comes up with prospective solutions by slogging methodically through relevant possibilities; and again, the generator
itself then defines the effective search space.
Given a concrete system with procedures for generating and testing potential
solutions, the basic structure of search becomes an alternating cycle: the generator proposes a candidate, and the tester
checks it out. If the test succeeds, the search is finished; if not, the system returns to the generator and goes around again
(at least until the search space is exhausted)."
Due to the huge number of possible paths to the solution, our search efforts must limit the paths
investigated by the use of heuristics. For Haugeland, it is the intelligent creation of heuristics that represent
the real work involved in creating software that makes machines behave intelligently.
p.178-179"In one way or another, controlling or circumventing combinatorial explosion has been a
central concern of Artificial Intelligence from its inception; the issue is broad and deep.
In general, therefore, search must be selective, that is, partial and risky.
The crucial insight, however, is that the selection need not be random. Newell, Shaw, and Simon propose that problem-solving
search always follows heuristic guidelines... thereby dramatically improving the odds; they even suggest that the
degree of improvement (over random chance) is one measure of a system's intelligence. Applying such heuristics, then, is what
it means to think about a hard problem, trying to find a solution. And the challenge of designing an intelligent
machine reduces to the chore of figuring out and implementing suitably 'powerful' heuristics for it to employ."
The design of an effective, selective search heuristic has a good chance to form the core
of a successful computer program for an application in the field of Artificial Intelligence. The heuristic proposed
in this paper uses a different method [time will prove whether or not it is better than other methods] to focus
the search efforts of the machine, and is an attempt to tame the combinatorial explosion of possible search paths.
p.184"The idea of using explicit selection heuristics to tame the combinatorial explosion is a major
intellectual milestone. It was perhaps the crucial element in actually launching the field of Artificial Intelligence, and
it has been a conceptual mainstay ever since."
For our machine to be truly intelligent, we need to be able to evaluate the chances that a certain
situation is to our advantage - a situation that we might never have experienced, and which might differ in subtle ways from
previous experiences.
p.186"Genuine intelligence calls for a fuller, more versatile familiarity with the objects and events
within its ken [mental perception]."
|
