Information Technology Reference
In-Depth Information
Temporal uncertainty, as the name suggests, arises out of
imperfect foresight
- i.e. it concerns
the general problem of determining the future decision state of a dynamic system the
current and past decision states of which are known. As a sub-category of temporal
uncertainty, parametric uncertainty is that form of uncertainty the resolution of which
wholly depends on estimating a set of underlying parameters that determine a future
decision state of a system given its current and/or past decision states. The fundamental
premise is that there exist parameters, which if estimated accurately, would fully explain the
temporal transition from current to a future decision state. In most practical
AI
applications
it is handled by embedding an efficient parameter estimation
kernel
e.g. an asset price
prediction kernel that is embedded within an intelligent financial trading system (Huang,
Pasquier and Quek, 2009). On the other hand non-parametric uncertainty is that form of
temporal uncertainty the resolution of which is either wholly or substantially independent
of any parameters that can be statistically estimated from the current or past decision states
of the system. That is, in resolving non-parametric uncertainty one cannot assume that there
is a set of parameters whose accurate estimation can fully explain the dynamic system's
time-path (Kosut, Lau and Boyd, 1992). To resolve non-parametric uncertainty,
AI
models
are usually equipped with some feedback/learning mechanism coupled with a
performance
measure index
that indicates when optimal learning has occurred so that predictive utility
isn't lost on account of
overtraining
when predicting a future state using the current/past
states as the
inputs
(Yang et al, 2010).
Knowledge uncertainty, again as the name suggests, arises out of
imperfect understanding
-
i.e. it concerns the general problem of determining the future decision state of a dynamic
system the knowledge about whose current and/or past states are either
incomplete
,
ill-
defined
or
inconsistent
. If there is incomplete information available about the current decision
state of the system then the sub-category of knowledge uncertainty it would be categorized
under is informational uncertainty. A common way of dealing with informational
uncertainty is to try and
enhance
the current level of information by applying an appropriate
information theoretic tool e.g. Ding
et al
(2008) applied rough sets theory coupled with a
self-adaptive algorithm to separately “mine” consistent and inconsistent decision rules;
along with experimental validation for large incomplete information systems. If the
information available about the current decision state of the system is ill-defined i.e. it is
subject to
interpretational ambiguity
then it would come under the sub-category of linguistic
uncertainty. A large part of interpretational ambiguity arises as a direct result of statements
made in natural language (Walley and Cooman, 2001). Lotfi Zadeh, the proponent of
fuzzy
logic
, contended that
possibility measures
are best used to resolve linguistic uncertainty in
decision systems (Zadeh, 1965). If the information available about the current decision state
of the system is inconsistent i.e. it is fundamentally dependent on the origin, then the
resulting uncertainty would come under the sub-category of paradigmatic uncertainty. If
available information is dependent on its origin then it can be expected to materially change
if one chooses a different source for the same information. For example, software agents
have to reason and act on a domain in which the universe of possible scenarios is
fundamentally prescribed by the available metadata records. But these metadata records can
sometimes be found to be mutually inconsistent when compared. The paradigmatic
uncertainty resulting from the inconsistency and imprecision is best addressed by building
in enough flexibility in the system so that the cogency of information related to the current
