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Artificial intelligence (AI)
Logical reasoning and problem solving
The ability to reason logically is an important aspect of intelligence and has always been
a major focus of AI research. An important landmark in this area was a theorem-proving
program written in 1955–56 by Allen Newell and J. Clifford Shaw of the RAND
Corporation and Herbert Simon of the Carnegie Mellon University. The Logic Theorist,
as the program became known, was designed to prove theorems from Principia
Mathematica (1910–13), a three-volume work by the British philosopher-mathematicians
Alfred North Whitehead and Bertrand Russell. In one instance, a proof devised by the
program was more elegant ... (100 of 8538 words)
1. Logic and Artificial Intelligence
1.1 The Role of Logic in Artificial Intelligence
Theoretical computer science developed out of logic, the theory of computation (if this is
to be considered a different subject from logic), and some related areas of mathematics.[4]
So theoretically minded computer scientists are well informed about logic even when
they aren't logicians. Computer scientists in general are familiar with the idea that logic
provides techniques for analyzing the inferential properties of languages, and with the
distinction between a high-level logical analysis of a reasoning problem and its
implementations. Logic, for instance, can provide a specification for a programming
language by characterizing a mapping from programs to the computations that they
license. A compiler that implements the language can be incomplete, or even unsound, as
long as in some sense it approximates the logical specification. This makes it possible for
the involvement of logic in AI applications to vary from relatively weak uses in which
the logic informs the implementation process with analytic insights, to strong uses in
which the implementation algorithm can be shown to be sound and complete. In some
cases, a working system is inspired by ideas from logic, but acquires features that at first
seem logically problematic but can later be explained by developing new ideas in logical
theory. This sort of thing has happened, for instance, in logic programming.
In particular, logical theories in AI are independent from implementations. They can be
used to provide insights into the reasoning problem without directly informing the
implementation. Direct implementations of ideas from logic—theorem-proving and
model-construction techniques—are used in AI, but the AI theorists who rely on logic to
model their problem areas are free to use other implementation techniques as well. Thus,
in Moore 1995b (Chapter 1), Robert C. Moore distinguishes three uses of logic in AI; as a
tool of analysis, as a basis for knowledge representation, and as a programming language.
A large part of the effort of developing limited-objective reasoning systems goes into the
management of large, complex bodies of declarative information. It is generally
recognized in AI that it is important to treat the representation of this information, and the
reasoning that goes along with it, as a separate task, with its own research problems.
The evolution of expert systems illustrates the point. The earliest expert systems, such as
MYCIN (a program that reasons about bacterial infections, see Buchanan & Shortliffe
1984), were based entirely on large systems of procedural rules, with no separate
representation of the background knowledge—for instance, the taxonomy of the
infectious organisms about which the system reasoned was not represented.
Later generation expert systems show a greater modularity in their design. A separate
knowledge representation component is useful for software engineering purposes—it is
much better to have a single representation of a general fact that can have many different
uses, since this makes the system easier to develop and to modify. And this design turns
out to be essential in enabling these systems to deliver explanations as well as mere
conclusions.[5]
1.2 Knowledge Representation
In response to the need to design this declarative component, a subfield of AI known as
knowledge representation emerged during the 1980s. Knowledge representation deals
primarily with the representational and reasoning challenges of this separate component.
The best place to get a feel for this subject is the proceedings of the meetings that are now
held every other year: see Brachman et al. 1989, Allen et al. 1991, Nebel et al. 1992,
Doyle et al. 1994, Aiello et l. 1996, Cohn . 1998, Cohn et l. 2000, and Fensel et l. 2002.
Typical articles in the proceedings of the KR and Reasoning conferences deal with the
following topics.
1. Topics in logical theory and the theory of computation, including
a. Nonmonotonic logic
b. Complexity theory
2. Studies in application areas, including
a. Temporal reasoning
b. Formalisms for reasoning about planning, action and change
c. Metareasoning
d. Reasoning about context
e. Reasoning about values and desires
f. Reasoning about the mental states of other agents, and especially about
knowledge and belief
g. Spatial reasoning
h. Reasoning about vagueness
3. Studies in application techniques, including
a. Logic programming
b. Description logics
c. Theorem proving
d. Model construction
4. Studies of large-scale applications, including
a. Cognitive robotics
b. Merging, updating, and correcting knowledge bases
These topics hardly overlap at all with the contents of the Journal of Symbolic Logic, the
principal research archive for mathematical logic. But there is substantial overlap in
theoretical emphasis with The Journal of Philosophical Logic, where topics such as tense
logic, epistemic logic, logical approaches to practical reasoning, belief change, and
vagueness account for a large percentage of the contributions. Very few JPL publications,
however, deal with complexity theory or with potential applications to automated
reasoning.
1.5 The Role of Artificial Intelligence in Logic
The importance of applications in logical AI, and the scale of these applications,
represents a new methodology for logic—one that would have been impossible without
mechanized reasoning. This methodology forces theoreticians to think through problems
on a new scale and at a new level of detail, and this in turn has a profound effect on the
resulting theories. The effects of this methodology will be illustrated in the sections
below, dealing with various topics in logical AI. But the point is illustrated well by
reasoning about action and change. This topic was investigated in the philosophical
literature. Reasoning about change, at least, is part of tense logic, and the consequences
of action are investigated in the literature on “seeing to it that”; see, for instance, Belnap
1996. The latter theory has no very robust account of action. The central construct is a
variation on a branching-time modality of the sort that has been familiar since Prior 1967.
Although it represents an interesting development in philosophical logic, the scale of the
accomplishment is very different from the research tradition in logical AI reported in
Section 4, below. The formalisms in this tradition not only support the formalization of
complex, realistic planning problems, but provide entirely new insights into reasoning
about the causal effects of actions, the persistence of states, and the interactions between
actions and continuous physical processes. Developments such as this would have been
impossible without the interactions between the logical theories and large-scale, practical
applications in automated planning.
In Carnap 1955, Rudolf Carnap attempted to clarify intensional analyses of linguistic
meaning, and to justify from a methodological point of view, by imagining how the
analysis could be applied to the linguistic usage of a hypothetical robot. Carnap hoped
that the fact that we could imagine ourselves to know the internal structure of the robot
would help to make the case for an empirical science of semantics more plausible. This
hope proved to be unjustified; the philosophical issue that concerned Carnap remains
controversial to this day, and thought experiments with robots have not proved to be
particularly rewarding in addressing it. Real robots, though, with real applications,[9] are a
very different matter. Though it is hard to tell whether they will prove to be helpful in
clarifying fundamental philosophical problems, they provide a laboratory for logic that is
revolutionary in its potential impact on the subject. They motivate the development of
entirely new logical theories that I believe will prove to be as important for philosophy as
the fundamental developments of the late nineteenth century proved to be.
The emergence of separate mathematical and philosophical subspecialties within logic
was not an entirely healthy thing for the field. The process of making mathematical logic
rigorous and of demonstrating the usefulness of the techniques in pursuing mathematical
ends that was pursued so successfully in the first half of the twentieth century represents
a coherent refinement of logical methodology. All logicians should be pleased and proud
that logic is now an area with a body of results and problems that is as substantial and
challenging as those associated with most areas of mathematics.
But these methodological advances were gained at the expense of coverage. In the final
analysis, logic deals with reasoning—and relatively little of the reasoning we do is
mathematical, while almost all of the mathematical reasoning that nonmathematicians do
is mere calculation. To have both rigor and scope, logic needs to keep its mathematical
and its philosophical side united in a single discipline. In recent years, neither the
mathematical nor the philosophical professions—and this is especially true in the United
States—have done a great deal to promote this unity. But the needs of Computer Science,
provide strong unifying motives. The professional standards for logical research in
Computer Science certainly require rigor, but the field also puts its practitioners into
contact with reasoning domains that are not strictly mathematical, and creates needs for
innovative logical theorizing.
The most innovative and ambitious area of Computer Science, in terms of its coverage of
reasoning, and the one that is closest in spirit to philosophical logic, is AI. This article
will attempt to provide an introduction, for outsiders who are familiar with logic, to the
aspects of AI that are closest to the philosophical logic tradition. This area of logic
deserves, and urgently needs, to be studied by historians. But I am not a historian, and
this entry does not pretend to be a history.
MODEL QUESTION:
1. What is the term used for describing the judgmental or commonsense part of problem
solving?
A.Heuristic(ANSWER)
B. Critical
C. Value based
D.Analytical
E. None of the above
2. What stage of the manufacturing process has been described as "the mapping of
function onto form"?
A.Design (ANSWER)
B. Distribution
C. project management
D.field service
E. None of the above
3. Which kind of planning consists of successive representations of different levels of a
plan?
A.hierarchical planning (ANSWER)
B. non-hierarchical planning
C. All of the above
D.project planning
E. None of the above
4. What was originally called the "imitation game" by its creator?
A.The Turing Test(ANSWER)
B. LISP
C. The Logic Theorist
D.Cybernetics
E. None of the above
5. Decision support programs are designed to help managers make:
A.budget projections
B. visual presentations
C. business decisions (ANSWER)
D.vacation schedules
E. None of the above
6. PROLOG is an AI programming language which solves problems with a form of
symbolic logic known as predicate calculus. It was developed in 1972 at the University of
Marseilles by a team of specialists. Can you name the person who headed this team?
A.Alain Colmerauer (ANSWER)
B. Nicklaus Wirth
C. Seymour Papert
D.John McCarthy
E. None of the above
7. Programming a robot by physically moving it through the trajectory you want it to
follow is called:
A.contact sensing control
B. continuous-path control (ANSWER)
C. robot vision control
D.pick-and-place control
E. None of the above
8. To invoke the LISP system, you must enter
A.AI
B. LISP
C. CL (Common Lisp)
D.both b and c (ANSWER)
E. None of the above
9. DEC advertises that it helped to create "the world's first expert system routinely used
in an industrial environment," called XCON or:
A.PDP-11
B. Rl (ANSWER)
C. VAX
D.MAGNOM
E. None of the above
10. Prior to the invention of time sharing, the prevalent method of computer access was:
A.batch processing (ANSWER)
B. telecommunication
C. remote access
D.All of the above
E. None of the above
11. Seymour Papert of the MIT AI lab created a programming environment for children
called:
A.BASIC
B. LOGO (ANSWER)
C. MYCIN
D.FORTRAN
E. None of the above
12. The Strategic Computing Program is a project of the:
A.Defense Advanced Research Projects Agency (ANSWER)
B. National Science Foundation
C. Jet Propulsion Laboratory
D.All of the above
E. None of the above
13. The original LISP machines produced by both LMI and Symbolics were based on
research performed at:
A.CMU
B. MIT (ANSWER)
C. Stanford University
D.RAMD
E. None of the above
14. In LISP, the addition 3 + 2 is entered as
A.3 + 2
B. 3 add 2
C. 3 + 2 =
D.(+ 3 2) (ANSWER)
E. None of the above
15. Weak AI is
A.the embodiment of human intellectual capabilities within a computer.
a set of computer programs that produce output that would be considered to reflect
B.
intelligence if it were generated by humans.
the study of mental faculties through the use of mental models implemented on a
C.
computer. (ANSWER)
D.All of the above
E. None of the above
16.In LISP, the function assigns the symbol x to y is
A.(setq y x)
B. (set y = 'x')
C. (setq y = 'x')
D.(setq y 'x') (Answer)
E. None of the above
17. In LISP, the function returns t if <object> is a CONS cell and nil otherwise:
A.(cons <object>)
B. (consp <object>) (answer)
C. (eq <object>)
D.(cous = <object>)
E. None of the above
18. In a rule-based system, procedural domain knowledge is in the form of:
A.production rules(ANSWER)
B. rule interpreters
C. meta-rules
D.control rules
E. None of the above
19. If a robot can alter its own trajectory in response to external conditions, it is
considered to be:
A.Intelligent (ANSWER)
B. mobile
C. open loop
D.non-servo
E. None of the above
20. One of the leading American robotics centers is the Robotics Institute located at:
A.CMU (ANSWER)
B. MIT
C. RAND
D.SRI
E. None of the above
21. In LISP, the function returns the first element of a list Is
A.set
B. Car (ANSWER)
C. first
D.second
E. None of the above
22. Nils Nilsson headed a team at SRI that created a mobile robot named:
A.Robitics
B. Dedalus
C. Shakey (ANSWER)
D.Vax
E. None of the above
23. An AI technique that allows computers to understand associations and relationships
between objects and events is called:
A.heuristic processing
B. cognitive science
C. relative symbolism
D.pattern matching (ANSWER)
E. None of the above
24. The new organization established to implement the Fifth Generation Project is called:
A.ICOT (Institute for New Generation Computer Technology) (ANSWER)
B. MITI (Ministry of International Trade and Industry)
C. MCC (Microelectronics and Computer Technology Corporation)
D.SCP (Stategic Computing Program)
E. None of the above
25. The field that investigates the mechanics of human intelligence is:
A.history
B. cognitive science (ANSWER)
C. psychology
D.sociology
E. None of the above
26. A problem is first connected to its proposed solution during the _____ stage.
A.conceptualization
B. identification
C. Formalization (ANSWER)
D.implementation.
E. testing
27. What is the name of the computer program that simulates the thought processes of
human beings?
A.Human logic
B. Expert reason
C. Expert system (ANSWER)
D.Personal information
E. None of the above
28. What is the name of the computer program that contains the distilled knowledge of
an expert?
A.Data base management system
B. Management information System
C. Expert system (ANSWER)
D.Artificial intelligence
E. None of the above
29. Claude Shannon described the operation of electronic switching circuits with a
system of mathematical logic called:
A.LISP
B. XLISP
C. Boolean algebra (ANSWER)
D.neural networking
E. None of the above
30. A computer program that contains expertise in a particular domain is called an:
A.intelligent planner
B. automatic processor
C. expert system (ANSWER)
D.operational symbolizer
E. None of the above
31. Ambiguity may be caused by:
A.syntactic ambiguity
B. multiple word meanings
C. unclear antecedents
D.All of the above (ANSWER)
E. None of the above
32. Which company offers the LISP machine considered to be "the most powerful
symbolic processor available"?
A.LMI
B. Symbolics (ANSWER)
C. Xerox
D.Texas Instruments
E. None of the above
33. What of the following is considered to be a pivotal event in the history of AI.
A.1949, Donald O, The organization of Behaviour,
B. 1950, Computing Machinery and Intelligence.
C. 1956, Dartmouth University Conference Organized by John McCarthy (ANSWER)
D.1961, Computer and Computer Sense.
E. None of the above
34. Natural language processing is divided into the two subfields of:
A.symbolic and numeric
B. time and motion
C. algorithmic and heuristic
D.understanding and generation (ANSWER)
E. None of the above
35. High-resolution, bit-mapped displays are useful for displaying:
A.clearer characters
B. graphics
C. more characters
D.All of the above (ANSWER)
E. None of the above
36. A bidirectional feedback loop links computer modelling with:
A.artificial science
B. heuristic processing
C. human intelligence
D.cognitive science (ANSWER)
E. None of the above
37. Which of the following have people traditionally done better than computers?
A.recognizing relative importance
B. finding similarities
C. resolving ambiguity
D.All of the above (ANSWER)
E. (a) and (c) above.
38. In LISP, the function evaluates both <variable> and <object> is
A.Set (ANSWER)
B. setq
C. add
D.eva
E. None of the above
39. Which type of actuator generates a good deal of power but tends to be messy?
A.electric
B. Hydraulic (ANSWER)
C. pneumatic
D.(B) and (c) above
E. None of the above
40. Research scientists all over the world are taking steps towards building computers
with circuits patterned after the complex inter connections existing among the human
brain's nerve cells. What name is given to such type of computers?
A.Intelligent computers
B. Supercomputers
C. Neural network computers (ANSWER)
D.Smart computers
E. None of the above
41. The hardware features of LISP machines generally include:
A.large memory and a high-speed processor
B. letter-quality printers and 8-inch disk drives
C. a mouse and a specialized keyboard
D.both (a) and (c) (ANSWER)
E. None of the above
42. In 1985, the famous chess player David Levy beat a world champion chess program
in four straight games by using orthodox moves that confused the program. What was the
name of the chess program?
A.Kaissa
B. CRAY BLITZ (ANSWER)
C. Golf
D.DIGDUG
E. None of the above
43. The explanation facility of an expert system may be used to:
A.construct a diagnostic model
B. expedite the debugging process
C. explain the system's reasoning process
D.All of the above
E. both (b) and (c) (ANSWER)
44. A process that is repeated, evaluated, and refined is called:
A.diagnostic
B. descriptive
C. interpretive
D.Iterative (ANSWER)
E. None of the above
45. Visual clues that are helpful in computer vision include:
A.colour and motion
B. depth and texture
C. height and weight
D.a and b above (ANSWER)
E. None of the above
46. In LISP, the function X (x). (2x+l) would be rendered as
A.(lambda (x) (+(*2 x)l)) (ANSWER)
B. (lambda (x) (+1 (* 2x)
C. (+ lambda (x) 1 (*2x))
D.(* lambda(x) (+2x1)
E. None of the above
47. A natural language generation program must decide:
A.what to say
B. when to say something
C. why it is being used
D.both (a) and (b) (ANSWER)
E. None of the above
48. Who is considered to be the "father" of artificial intelligence?
A.Fisher Ada
B. John McCarthy
C. Allen Newell
D.Alan Turning (ANSWER)
E. None of the above
49. In which of the following areas may ICAI programs prove to be useful?
A.educational institutions
B. corporations
C. department of Defence
D.All of the above (ANSWER)
E. None of the above
50. A network with named nodes and labeled arcs that can be used to represent certain
natural language grammars to facilitate parsing.
A.Tree Network
B. Star Network
C. Transition Network (ANSWER)
D.Complete Network
E. None of the above