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en:dydaktyka:planning:intro [2020/04/14 10:53] msl [Solutions] |
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| ====== Automated Planning: 101 ====== | ====== Automated Planning: 101 ====== | ||
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| + | <WRAP center round important 60%> | ||
| + | Due to the COVID-19 outbreak, all files related to this class are stored in the [[https://gitlab.com/agh-krr/2019-2020/labs-planning|Gitlab repository]]. This class uses files stored in the ''00_intro'' folder. Please refer to the ''Readme.md'' on how to submit the solutions. | ||
| + | </WRAP> | ||
| This class is an introduction to a very important and omnipresent (i.e.: industry, robotics, video games etc.) discipline of AI – planning. Planning consists in finding a series of actions (called “a plan”), which used on a initial state S will conclude in achieving goal state G. Every action has conditions in which it can be executed, and effects of its application. Planning is difficult: without adequate constraints on actions, the complexity reaches the ''PSPACE''-complete class (worse than ''NP'' problems class, which includes the problems of discrete optimization). Furthermore, real (not toylike) problems may contain additional factors increasing their difficulty: | This class is an introduction to a very important and omnipresent (i.e.: industry, robotics, video games etc.) discipline of AI – planning. Planning consists in finding a series of actions (called “a plan”), which used on a initial state S will conclude in achieving goal state G. Every action has conditions in which it can be executed, and effects of its application. Planning is difficult: without adequate constraints on actions, the complexity reaches the ''PSPACE''-complete class (worse than ''NP'' problems class, which includes the problems of discrete optimization). Furthermore, real (not toylike) problems may contain additional factors increasing their difficulty: | ||
