Hayes et al. is composed of a set of 20 papers covering these five areas:
These papers cover advanced topics in AI research, and a comprehensive treatment of them would be encyclopedic. However, one paper in particular, in our opinion, is worth looking at in some detail - because it nicely captures the spirit and, to an appreciable degree, the technical orientation of Hayes et al. and because it also interweaves the topics of defeasible reasoning, LP, implementation, metaprogramming and foundational issues (e.g., learning) central to the LAI-CAI clash. The paper in question is Chapter 8, ``Non-monotonic Learning'', by Bain and Mugleton. It introduces a non-monotonic formalism called closed world specialization. This formalism's implementation in Prolog results in an incremental learning system that revises its beliefs upon receiving new examples.
One way to learn from experience is to specialize ``over-general'' beliefs. Consider yet again the belief that all birds fly; it might be represented in LP by the following rule.
If the new conjunctive belief that an emu is a bird and emus can't fly were introduced, the above rule would no longer be true. The system would have to learn; it would have to specialize the rule so as to accurately represent the new belief. Incremental learning systems may generalize their belief sets in the face of a new example that these sets don't entail, or such systems may specialize their belief set if it is contradicted by a new example. Previous approaches tend to over-specialize in an attempt to adapt to contradictory examples. It's not hard to make this clear:
Consider the case where a
system's initial belief set, 
, includes the following statements.
>From the system (an LP system here) can conclude that
Since it is not true that flies(emu), the system must revise its beliefs so that
One way is to delete the clause flies(X) 
bird(X) and
produce the new belief set
Since 
is incomplete with respect to
flies(eagle), this set can be generalized to produce
The set 
, however, is an over-specialization.
The fact flies(sparrow)
is unfortunately not deducible from
To solve the problem of over-specialization in incremental learning systems, Bain and Mugleton define the most-general-correct-specialization (MGCS) and develop a resolution-based method for constructing an MGCS from an incorrect clausal theory (an intial set of beliefs plus the new contradictory example). Using meta-programming (see our discussion of this topic above) the authors demonstrate how this method can be implemented in Prolog.
The method exploits the LP notion of negation as failure.
A common (but limited) approach to non-monotonic reasoning is based on the
``Closed World Assumption'' (CWA) inference rule.
This rule states that if a statement p is not a logical consequence of
a theory, then infer 
.
Prolog implements this inference by interpreting failure as negation.
If Prolog fails to prove p, where p is a ground atom (a
fact with no variables), then Prolog infers .
An example demonstrates the results of this approach. Consider again the
initial belief set
and the fact that flies(emu) is not true. The proposed method
produces the revised belief set composed of
bird(X)
The method introduces a new predicate; in this case flightless.
Its negation (by
failure), not(flightless(X)), is added to the conditions for flies(X)
and
the fact flightless(emu) is added to the belief set. The result is
the revised belief set which doesn't incorrectly
entail flies(emu); and flies(sparrow)
can be correctly proved from 
{bird(sparrow)}.
Several other papers in the Hayes et al. volume (e.g., ``Inductive Formation of Programs and Descriptions'', yet another case of work that exploits on meta-programming) make considerable headway against the common but mistaken notion that learning is outside the reach of LAI but is the forté CAI.