Planning languages

The most commonly used languages for representing planning problems, such as STRIPS and PDDL for Classical Planning, are based on state variables. Each possible state of the world is an assignment of values to the state variables, and actions determine how the values of the state variables change when that action is taken. Since a set of state variables induce a state space that has a size that is exponential in the set, planning, similarly to many other computational problems, suffers from the curse of dimensionality (прокляття розмірності (у математичному програмуванні) and the combinatorial explosion.

An alternative language for describing planning problems is that of hierarchical task networks, in which a set of tasks is given, and each task can be either realized by a primitive action or decomposed into a set of other tasks. This does not necessarily involve state variables, although in more realistic applications state variables simplify also the description of task networks.

3. Vision: scene recognition, object recognition, face recognition.

Computer vision is a field that includes methods for acquiring, processing, analyzing, and understanding images and, in general, high-dimensional data from the real world in order to produce numerical or symbolic information. A theme in the development of this field has been to duplicate the abilities of human vision by electronically perceiving and understanding an image. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory. Computer vision has also been described as the enterprise of automating and integrating a wide range of processes and representations for vision perception.

Applications range from tasks such as industrial machine vision systems which, say, inspect bottles speeding by on a production line, to research into artificial intelligence and computers or robots that can comprehend the world around them. The computer vision and machine vision fields have significant overlap. Computer vision covers the core technology of automated image analysis which is used in many fields. Machine vision usually refers to a process of combining automated image analysis with other methods and technologies to provide automated inspection and robot guidance in industrial applications.

As a scientific discipline, computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, or multidimensional data from a medical scanner.

As a technological discipline, computer vision seeks to apply its theories and models to the construction of computer vision systems. Examples of applications of computer vision include systems for:

· Controlling processes, e.g., an industrial robot;

· Navigation, e.g., by an autonomous vehicle or mobile robot;

· Detecting events, e.g., for visual surveillance or people counting;

· Organizing information, e.g., for indexing databases of images and image sequences;

· Modeling objects or environments, e.g., medical image analysis or topographical modeling;

· Interaction, e.g., as the input to a device for computer-human interaction;

· Automatic inspection, e.g., in manufacturing applications.

Sub-domains of computer vision include: scene reconstruction, event detection, video tracking, object recognition, learning, indexing, motion estimation, and image restoration.

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