We can evaluate an algorithm's performance in four ways:
Completeness: Is the algorithm guaranteed to find a solution when there is one?
Optimality: Does the strategy find the optimal solution?
Time complexity: How long does it take to find a solution?
Space complexity: How much memory is needed to perform the search? (查看原文)
AI currently encompasses a huge variety of subfields, ranging from the general (learning and perception) to the specific, such as playing chess, proving mathematical theorems, writing poetry, driving a car on a crowded street, and diagnosing diseases. AI is relevant to any intellectual task; it is truly a universal field. (查看原文)
The Turing Test, proposed by Alan Turing (1950), was designed to provide a satisfactory operational definition of intelligence. A computer passes the test if a human interrogator, after posing some written questions, cannot tell whether the written responses come from a person or from a computer. (查看原文)
•natural language processing to enable it to communicate successfully in English;
•knowledge representation to store what it knows or hears;
•automated reasoning to use the stored information to answer questions and to draw new conclusions;
•machine learning to adapt to new circumstances and to detect and extrapolate patterns.
the so-called total Turing Test includes a video signal so that the interrogator can test the subject's perceptual abilities, as well as the opportunity for the interrogator to pass physical objects "through the hatch." To pass the total Turing Test, the computer will need
• computer vision to perceive objects, and
• robotics to manipulate objects and move about. (查看原文)
Aristotle (384-322 B.C.), whose bust appears on the front cover of this book, was the first to formulate a precise set of laws governing the rational part of the mind. He developed an informal system of syllogisms for proper reasoning, which in principle allowed one to generate conclusions mechanically, given initial premises. (查看原文)
Notice that we said environment states, not agent states. if we define success in terms of agent's opinion of its own performance, an agent could achieve perfect rationality simply by deluding itself that its performance was perfect. Human agents in particular are notorious for "sour grapes"—believing they did not really want something (e.g., a Nobel Prize) after not getting it (查看原文)
We can evaluate an algorithm's performance in four ways:
•Completeness: Is the algorithm guaranteed to find a solution when there is one?
•Optimality: Does the strategy find the optimal solution, as defined on page 68?
•Time complexity: How long does it take to find a solution?
•Space complexity: How much memory is needed to perform the search? (查看原文)
•Before an agent can start searching for solutions, a goal must be identified and a well-defined problem must be formulated.
•A problem consists of five parts: the initial state, a set of actions, a transition model describing the results of those actions, a goal test function, and a path cost function. The environment of the problem is represented by a state space. A path through the
state space from the initial state to a goal state is a solution.
•Search algorithms treat states and actions as atomic: they do not consider any internal structure they might possess.
•Uninformed search methods have access only to the problem definition. The basic algorithms are as follows:
—Breadth-first search expands the shallowest nodes first; it is complete, optimal for unit step costs. but has exponen... (查看原文)
Why couldn’t all the work done in AI have taken place under the name of control theory or operations research or decision theory, which, after all, have objectives similar to those of AI? Or why isn’t AI a branch of mathematics? The first answer is that AI from the start embraced the idea of duplicating human faculties such as creativity, self-improvement, and language use. None of the other fields were addressing these issues. The second answer is methodology. AI is the only one of these fields that is clearly a branch of computer science (although operations research does
share an emphasis on computer simulations), and AI is the only field to attempt to build machines that will function autonomously in complex, changing environments. (查看原文)
