Titlebook: Artificial Intelligence Through Search; Christopher James Thornton,Benedict Boulay Book 1992 Springer Science+Business Media Dordrecht 199

珍珠無

書目名稱Artificial Intelligence Through Search影響因子(影響力)




書目名稱Artificial Intelligence Through Search影響因子(影響力)學(xué)科排名




書目名稱Artificial Intelligence Through Search網(wǎng)絡(luò)公開度




書目名稱Artificial Intelligence Through Search網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Artificial Intelligence Through Search被引頻次




書目名稱Artificial Intelligence Through Search被引頻次學(xué)科排名




書目名稱Artificial Intelligence Through Search年度引用




書目名稱Artificial Intelligence Through Search年度引用學(xué)科排名




書目名稱Artificial Intelligence Through Search讀者反饋




書目名稱Artificial Intelligence Through Search讀者反饋學(xué)科排名

NEEDY

Ossification

Problem Reduction (AND/OR-tree search),In the previous chapters we have dealt exclusively with different kinds of state-space search. In that representation, there was a starting state, a goal state and intermediate states linked implicitly via a successor function. The general shape of all states was the same. They were distinguished by the values of their internal components.

Melanocytes

Theron Muller,John Adamson,Steven Herderng a sentence or planning a sequence of robot actions. . is one of the central issues in problem-solving systems. It becomes so whenever the system, through lack of knowledge, is faced with a choice from a number of alternatives, where each choice leads to the need to make further choices, and so on

Nebulous

Addressing Spoken Fluency in the Classroomnal structure and is represented simply by a token. This process is subsumed in the general class of . search and is dealt with more generally in the next chapter. We will use the example of finding a path using a map. It is easy to describe and corresponds fairly closely to the non-technical meanin

avulsion

Erik Jon Byker,Matthew A. Witensteinn of a physical environment to find a path from a start node to a goal node. This sort of program obviously has uses for any task which involves route planning (e.g. programming mobile robots). But in fact the program we have constructed has many other possibilities.

恫嚇

https://doi.org/10.1007/978-3-031-47798-0earch space systematically but with no inkling of where a goal node is likely to be found. Recall that the programs have a “worm’s-eye” rather than a “bird’s-eye” view of the space. They just work through the space, methodically checking out every single possibility. For simple problems (small searc

HUSH

School Development in Tough Times, in a two-person game (e.g. chess) just need to decide at any given stage which of the possible moves is the best one. If we have a heuristic function which says how close a given state is to a goal state then the best move can be identified as the one which has the highest value of the heuristic fu

想象

先鋒派