|
p.1 Reasoning about any realistic domain always requires that some
simplification be made. The very act of preparing knowledge to support reasoning requires that we leave many
facts unknown, unsaid, or crudely summarized... An alternative to the extremes of ignoring or enumerating exceptions
is to summarize them, i.e., provide some warning signs to indicate which areas of the minefield are more dangerous than others.
Summarization is essential if we wish to find a reasonable compromise between safety and speed of movement.
This book studies a language in which summaries of exceptions in the minefield of judgment and belief can be represented and
processed.
p.12 Our goal is to make intensional systems [Intensional
systems deal with uncertainty in a context sensitive manner. They try to model the interdependencies and relevance relationships
of the variables in the system. - JLJ] operational by making relevance relationships explicit, thus curing
the impotence of declarative statements such as P(B|A)=p [JLJ - notation for the statement: the probability of B given A is
p]. As mentioned earlier, the reason one cannot act on the basis of such declarations is that one must first make sure
that other items in the knowledge base are irrelevant to B and hence can be ignored. The trick, therefore, is to encode knowledge
in such a way that the ignorable is recognizable, or better yet, that the unignorable is quickly identified and is readily
accessible... In effect, what network representations offer is a dynamically updated list of all currently valid licenses
to ignore, and licenses to ignore constitute permissions to act.
p.13 A central requirement for managing intensional systems is to
articulate the conditions under which one item of information is considered relevant to another, given what we already
know, and to encode knowledge in structures that display these conditions vividly as the knowledge undergoes changes.
p.14 The aim of artificial intelligence is to provide a computational
model of intelligent behavior, most importantly, commonsense reasoning.
p.15 this book will try to communicate the idea that "probability
is not really about numbers; it is about the structure of reasoning," as Glenn Shafer recently wrote.
p.306 Clearly, the only practical way of doing planning in an uncertain
domain is to generate portions of the decision tree on the fly from more economical forms of knowledge...
The difficulty with such a scheme is that the construction of any decision tree requires three diverse sources of knowledge,
each organized by a different set of principles:
1. Causal knowledge about how events influence each other
in the domain.
2. Knowledge about what action sequences are feasible in
any given set of circumstances.
3. Normative knowledge about how desirable the consequences are.
... Influence diagrams are an attempt to capture all three knowledge
sources in one graphical representation.
p.311 an influence diagram can be evaluated by sequentially instantiating
the decision and observation nodes (in chronological order) while treating the remaining chance nodes as a Bayesian network
that supplies the probabilistic parameters necessary for tree evaluation.
p.313 6.3.1 Information Sources and Their Values: It is generally
accepted that information is a useful commodity, that acting in an informed fashion is preferable to acting under ignorance.
This is why people accumulate information when it is available and purchase information when it is scarce. People also possess
strong intuition about whether one information source is more valuable (more reliable and pertinent) than another... The
value of any information source is defined as the difference between the utilities of two optimal strategies, one providing
the freedom of choosing different actions for different source outcomes, the other providing no such freedom. This
criterion can be used to rate the usefulness of various information sources and to decide whether a piece of information is
worth acquiring.
p.318 6.4.1 Focusing Attention: Control is the process
of scheduling the activation of information sources, both external (e.g., acquiring new input) and internal (e.g.,
invoking rules or updating beliefs). Decision analysis provides a framework for scheduling all computational activities so
as to focus on specific goals - updating the belief in a target set of hypotheses, shifting attention to a new set, and terminating
the activity once we reach an acceptable level of confidence in a hypothesis.
The main reason for focusing attention on a select set of
target hypotheses is to economize the acquisition of new data. Let us imagine a subset S of the nodes (normally the
leaves) that are known to be sensory or observable nodes for a given problem domain (e.g., laboratory tests in medical diagnosis).
In general, the instantiation of any of these sensory nodes incurs a positive cost, and the utility of the information they
convey might be insufficient to justify this cost. Thus, it is important to decide which node in S should be instantiated
first, based on the information it contributes to the decision at hand, i.e., the target node. If utility information
is available, then the value node naturally is the target. If we lack utility information, we assign priorities to
pending information sources based on their degree of informativeness.
p.326 The task of controlling reasoning activities was formulated
as that of finding an optimal schedule for activating information sources. Decision theory provides a framework for
assessing the knowledge and computations needed to perform this optimization precisely. It turns out that the knowledge required
is often unavailable... Subgoaling strategies emerge as a reasonable compromise; they are computationally tractable...
they still provide a focused way of acquiring information.
|
