Active Logics: A Unified Formal Approach to Episodic Reasoning
Active Logics: A Unified Formal Approach to Episodic Reasoning
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Date
1999-10-14
Authors
Elgot-Drapkin, Jennifer
Kraus, Sarit
Miller, Michael
Nirkhe, Madhura
Perlis, Donald
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Abstract
Artificial intelligence research falls roughly into two categories:
formal and implementational. This division is not completely firm:
there are implementational studies based on (formal or informal)
theories (e.g., CYC, SOAR, OSCAR), and there are theories framed with
an eye toward implementability (e.g., predicate circumscription).
Nevertheless, formal/theoretical work tends to focus on very narrow
problems (and even on very special cases of very narrow problems) while
trying to get them ``right'' in a very strict sense, while
implementational work tends to aim at fairly broad ranges of behavior
but often at the expense of any kind of overall conceptually unifying
framework that informs understanding. It is sometimes urged that this
gap is intrinsic to the topic: intelligence is not a unitary thing for
which there will be a unifying theory, but rather a ``society'' of
subintelligences whose overall behavior cannot be reduced to useful
characterizing and predictive principles.
Here we describe a formal architecture that is more closely tied to
implementational constraints than is usual for formalisms, and which
has been used to solve a number of commonsense problems in a unified
manner. In particular, we address the issue of formal, integrated, and
longitudinal reasoning: inferentially-modeled behavior that
incorporates a fairly wide variety of types of commonsense reasoning
within the context of a single extended episode of activity requiring
keeping track of ongoing progress, and altering plans and beliefs
accordingly. Instead of aiming at optimal solutions to isolated,
well-specified and temporally narrow problems, we focus on satisficing
solutions to under-specified and temporally-extended problems, much
closer to real-world needs. We believe that such a focus is required
for AI to arrive at truly intelligent mechanisms with the ability to
behave effectively over considerably longer time periods and range of
circumstances than is common in AI today. While this will surely lead
to less elegant formalisms, it also surely is requisite if AI is to get
fully out of the blocks-world and into the real world.
(Also cross-referenced as UMIACS-TR-99-65)