explain four functions of evaluation
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The main aim of teaching learning process is to enable the pupil to achieve intended learning outcomes. In this process the learning objectives are fixed then after the instruction learning progress is periodically evaluated by tests and other evaluation devices.
Answer:
An evaluation function, also known as a heuristic evaluation function or static evaluation function, is a function used by game-playing computer programs to estimate the value or goodness of a position (usually at a leaf or terminal node) in a game tree. A tree of such evaluations is usually part of a minimax or related search paradigm which returns a particular node and its evaluation as a result of alternately selecting the most favorable move for the side on move at each ply of the game tree. The value is a quantized scalar, often in nths of the value of a playing piece such as a stone in go or a pawn in chess. n may be tenths, hundredths or other convenient fraction.
The value is presumed to represent the relative probability of winning if the game tree were expanded from that node to the end of the game. The function looks only at the current position (i.e. what spaces the pieces are on and their relationship to each other) and does not take into account the history of the position or explore possible moves forward of the node (therefore static). This implies that for dynamic positions where tactical threats exist, the evaluation function will not be an accurate assessment of the position. These positions are termed non-quiescent; they require at least a limited kind of search extension called quiescence search to resolve threats before evaluation. Some values returned by evaluation functions are absolute rather than heuristic, if a win, loss or draw occurs at the node.
There do not exist analytical or theoretical models for evaluation functions for unsolved games, nor are such functions entirely ad-hoc. The composition of evaluation functions is determined empirically by inserting a candidate function into an automaton and evaluating its subsequent performance. A significant body of evidence now exists for several games like chess, shogi and go as to the general composition of evaluation functions for them.
The general approach for constructing evaluation functions is as a linear combination of various weighted terms determined to influence the value of a position. Each term may be considered to be composed of first order factors (those that depend only on the space and any piece on it), second order factors (the space in relation to other spaces), and nth-order factors (dependencies on history of the position
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