p.3 If we are going to say that a given program thinks like a human, we
must have some way of determining how humans think. We need to get inside the actual workings of human minds. There
are two ways to do this: through introspection - trying to catch our own thoughts as they go by - and through psychological
experiments. Once we have a sufficiently precise theory of the mind, it becomes possible to express the theory as
a computer program.
p.5 One important point to keep in mind: We will see before too long
that achieving perfect rationality - always doing the right thing - is not feasible in complicated environments. The computational
demands are too high. For most of the book, however, we will adopt the working hypothesis that perfect rationality is a good
starting point for analysis. It simplifies the problem and provides the appropriate setting for most of the foundational material
in the field. Chapters 6 and 17 deal explicitly with the issue of limited rationality - acting appropriately when
there is not enough time to do all the computations one might like.
p.32 An agent is anything that can be viewed as perceiving
its environment through sensors and acting on the environment through actuators... We use the term percept
to refer to the agent's perceptual inputs at any given instant. An agent's percept sequence is the complete history
of everything the agent has ever perceived.
p.35 A performance measure embodies the criterion for success of
an agent's behavior. ...an agent ...generates a sequence of actions according to the percepts it receives.
p.36 a definition of a rational agent: For each percept sequence, a rational
agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept
sequence and whatever built-in knowledge the agent has.
p.95 Heuristic functions are the most common form in which additional knowledge of the problem is
imparted to the search algorithm.
p.107 A problem with fewer restrictions on the actions is called a relaxed problem. The cost of
an optimal solution to a relaxed problem is an admissible heuristic for the original problem.
p.108 A program called ABSOLVER can generate heuristics automatically from problem definitions, using "relaxed
problem" method and various other techniques (Prieditis, 1993). ABSOLVER generated a new heuristic for the 8-puzzle [a variation
of the famous Sam Loyd 15-puzzle] better than any preexisting heuristic and found the first useful heuristic for the famous
Rubik's cube puzzle. [JLJ - I wonder if ABSOLVER could be used to find an evaluation function for chess...]
p.162 Game playing was one of the first tasks undertaken in AI [Artificial Intelligence]. By 1950, almost
as soon as computers became programmable, chess had been tackled by Konrad Zuse (the inventor of the first programmable computer
and the first programming language), by Claude Shannon (the father of information theory), by Norman Wiener (the creator of
modern control theory), and by Alan Turing. Since then, there has been steady progress in the standard of play, to the point
that machines have surpassed humans in checkers and Othello, have defeated human champions (although not every time) in chess
and backgammon, and are competitive in many other games. The main exception is [the game] Go, in which computers perform at
the amateur level.
p.171 Shannon's 1950 paper, Programming a computer for playing chess, proposed
instead that programs should cut off the search earlier [not proceed to checkmate] and apply a heuristic evaluation function
to states in the search, effectively turning nonterminal nodes into terminal leaves. In other words, the suggestion
is to alter minimax or alpha-beta in two ways: the utility function is replaced by a heuristic evaluation function EVAL, which
gives an estimate of the position's utility, and the terminal test is replaced by a cutoff test that decides when
to apply EVAL...
p. 171 An evaluation function returns an estimate of the expected
utility of the game from a given position... It should be clear that the performance of a game-playing program is dependent
on the quality of its evaluation function. ...How exactly do we design good evaluation functions? First, the evaluation function
should order the terminal states in the same way as the true utility function... the evaluation function should be correlated
with the actual chances of winning.
p.194-195 This chapter introduces knowledge-based agents... Our final reason
for studying knowledge-based agents is their flexibility. They are able to accept new tasks in the form of explicitly described
goals, they can achieve competence quickly by being told or learning new knowledge about their environment, and they can adapt
to changes in the environment by updating the relevant knowledge... The central component of a knowledge-based agent is its
knowledge base, or KB.
p. 261 The knowledge engineering process
Knowledge engineering projects vary widely in content, scope, and difficulty,
but all such projects include the following steps:
1. Identify the task...
2. Assemble the relevant knowledge...
3. Decide on a vocabulary of predicates, functions, and constants...
4. Encode general knowledge about the domain...
5. Encode a description of the specific problem instance...
6. Pose queries to the inference procedure and get answers...
7. Debug the knowledge base...
p.386 It turns out that neither forward nor backward search is efficient without a good heuristic function.
Recall from Chapter 4 that a heuristic function estimates the distance from a state to the goal... The basic idea is to look
at the effects of the actions and at the goals that must be achieved and to guess how many actions are needed to achieve all
of the goals. Finding the exact number NP is hard, but it is possible to find reasonable estimates most of the time without
too much computation.
p.408 Planning systems are problem-solving algorithms that operate on explicit propositional (or first order)
representations of state and actions. These representations make possible the derivation of effective heuristics and the development
of powerful and flexible algorithms for solving problems.
p.464 Probability provides a way of summarizing the uncertainty that comes
from our laziness and ignorance.
p.466 The fundamental idea of decision theory is that an agent is rational
if and only if it chooses the action that yields the highest expected utility, averaged over all the possible outcomes of
the action. This is called the principle of Maximum Expected Utility (MEU)... the agent can make probabilistic predictions
of action outcomes and hence select the action with highest expected utility [usefulness].
p.585 If an agent maximizes a utility function that correctly reflects the
performance measure by which its behavior is being judged, then it will achieve the highest possible performance score if
we average over the environments in which the agent could be placed.
p.600 One of the most important parts of decision making is knowing what questions to ask.
p.601 The value of information derives from the fact that with the
information, one's course of action can be changed to suit the actual situation. One can discriminate according to
the situation, whereas without the information, one has to do what's best on average over the possible situations. In general,
the value of a given piece of information is defined to be the difference in expected value between best actions before and
after information is obtained.
p.602 In sum, information has value to the extent that it is likely to cause
a change of plan and to the extent the new plan will be significantly better than the old plan.
p.603 Implementing an information-gathering agent: A sensible agent should
ask questions of the user in a reasonable order, should avoid asking questions that are irrelevant, should take into account
the importance of each piece of information in relation to its cost, and should stop asking questions when that it appropriate.
All of these capabilities can be achieved by using the value of information as a guide.
p.863 Perception provides agents with information about the world
they inhabit. Perception is initiated by sensors. A sensor is anything that can record some aspect of the environment
and pass it as input to an agent program.
p.903 Sensors: Sensors are the perceptual interface between robots and their
environments. Passive sensors, such as cameras, are true observers of the environment: they capture signals that are generated
by other sources in the environment. Active sensors, such as sonar, send energy into the environment. They rely on
the fact that this energy is reflected back to the sensor. Active sensors tend to provide more information than passive
sensors, but at the expense of increased power consumption and with the danger of interference when multiple active sensors
are used at the same time.
p.969 Keeping track of the state of the world: This is
one of the core capabilities required of an intelligent agent. It requires both perception and updating of internal
representations.
