xvii what we need to study is what humans know. It is taken as a given that what allows humans
to behave intelligently is that they know a lot of things about a lot of things and are able to apply this knowledge as appropriate
to adapt to their environment and achieve their goals. So in the field of knowledge representation and reasoning we focus
on the knowledge, not on the knower. We ask what any agent - human, animal, electronic, mechanical - would
need to know to behave intelligently, and what sorts of computational mechanisms might allow its knowledge to be made available
to the agent as required.
p.1 One striking aspect of intelligent behavior is that it is clearly conditioned by knowledge:
for a very wide range of activities, we make decisions about what to do based on what we know (or believe) about the world,
effortlessly and unconsciously... Knowledge representation and reasoning, then, is that part of AI that is concerned with
how an agent uses what it knows in deciding what to do.
p.15 Before any system aspiring to intelligence can even begin to reason, learn, plan, or explain its behavior,
it must be able to formulate the ideas involved... So we need to start with a language of some sort, in terms of
which knowledge can be formulated.
p.24 What we are after is a system that can reason.
p.31-32 it is essential that we consider the overall architecture of the system we are about to create.
We must think ahead to what it is we ultimately want (or want our artificial agent) to compute. We need to make some commitments
to the reasons and times that inference will be necessary in our system's behavior. Finally, we need to stake out what is
sometimes called an ontology - the kinds of objects that will be important to the agent, the properties
those objects will be thought to have, and the relationships among them - before we can start populating our agent's
KB [Knowledge Base].
p.33 What sorts of objects will there be in our soap-opera world [JLJ - this is an extended
example of interacting objects used in the chapter to illustrate the concepts being discussed]?... Another set of
one-place predicates that is crucial for our domain representation is the set of attributes that our objects can
have... the next key predicates to consider... express relationships.
p.33-34 Now that we have our basic vocabulary in place, it is appropriate to start representing
the simple core facts of our soap-opera world... Once we have set down the types of each of our objects, we can capture
some of the properties of the objects. These properties will be the chief currency in talking about our domain, since we most
often want to see what properties (and relationships) are implied by a set of facts or conjectures.
p.37 Now that we have captured the basic structure of our soap-opera domain, it is time to turn to the reason
that we have done this representation in the first place: deriving implicit conclusions from our explicitly represented KB
[Knowledge Base].
p.44 Among the many types of facts in the soap-opera world that we have not captured are the following:...
statistical and probabilistic facts: These include those that involve portions of the sets
of individuals satisfying a predicate...default and prototypical facts: These cite characteristics that are
usually true, or reasonable to assume true unless told otherwise
