Wednesday, January 02, 2008
The Need for a Client View of Context-Independent Symbols
In the paper Interactively converging on context-sensitive representations: A solution to the frame problem by Robert M. French and Patrick Anselme, the authors describe a robotic simulation of context-sensitive representations:
"Agre and Chapman (1987) developed a simulation, Pengi, in which an agent — a penguin — makes use of context-dependent representations in order to avoid being attacked, in this case, by a bee. Piles of ice cubes are used to allow Pengi to protect himself from being stung by the bee. In addition, Pengi can fight back against the bee by means of a well-placed kick to any ice cube that the bee happens to be directly behind. As for the bee, it has two means to kill the penguin: either by striking an ice cube that Pengi is hiding behind or by stinging the penguin. Space does not allow a full development here of the strategies that the penguin can adopt, nor of his actual sensori-motor abilities (for a detailed description, see Agre, 1997). It is, however, important to note that the penguin has well-developed attentional capabilities that allow him to focus on the salient factors of the situation in which he finds himself.
In the Pengi simulation, all events are contextually determined. There are not context-independent representations, like “ice-cube wall” or even “bee.” The penguin’s representations are, to use Agre’s term, deictic, meaning that they depend on the circumstances in which they are used. Pengi’s representations take the form of the-wall-behind-which-I-will-hide or the-bee-I-want-to-kill (Agre, 1997, p. 267). These representations do not describe context-independent entities; rather they describe some aspect of the environment that is in a particular relation to the agent at a particular moment in time. In other words, rather than a context-independent “bee” representation, the system produces a representation for a particular bee in a particular place at a particular time. The Pengi model avoids the frame problem because the agent is manipulating context-sensitive representations for “bee” that include various salient aspects of situation at hand."
I suspect that biological agents show this kind of continuous context-sensitivity in their sense of the referents of symbols. As the "Great Global Graph" of linked data on the web grows, our automated agents will need to show this kind of selectivity as well. But rather than over-use the word "context", perhaps a better term here would be "personalization". So each clients view of the GGG, the world wide graph of data, will be different, skewed towards the needs of that client. This will probably be done by a collaboration between new kinds of search agents and each client themselves.
"Agre and Chapman (1987) developed a simulation, Pengi, in which an agent — a penguin — makes use of context-dependent representations in order to avoid being attacked, in this case, by a bee. Piles of ice cubes are used to allow Pengi to protect himself from being stung by the bee. In addition, Pengi can fight back against the bee by means of a well-placed kick to any ice cube that the bee happens to be directly behind. As for the bee, it has two means to kill the penguin: either by striking an ice cube that Pengi is hiding behind or by stinging the penguin. Space does not allow a full development here of the strategies that the penguin can adopt, nor of his actual sensori-motor abilities (for a detailed description, see Agre, 1997). It is, however, important to note that the penguin has well-developed attentional capabilities that allow him to focus on the salient factors of the situation in which he finds himself.
In the Pengi simulation, all events are contextually determined. There are not context-independent representations, like “ice-cube wall” or even “bee.” The penguin’s representations are, to use Agre’s term, deictic, meaning that they depend on the circumstances in which they are used. Pengi’s representations take the form of the-wall-behind-which-I-will-hide or the-bee-I-want-to-kill (Agre, 1997, p. 267). These representations do not describe context-independent entities; rather they describe some aspect of the environment that is in a particular relation to the agent at a particular moment in time. In other words, rather than a context-independent “bee” representation, the system produces a representation for a particular bee in a particular place at a particular time. The Pengi model avoids the frame problem because the agent is manipulating context-sensitive representations for “bee” that include various salient aspects of situation at hand."
I suspect that biological agents show this kind of continuous context-sensitivity in their sense of the referents of symbols. As the "Great Global Graph" of linked data on the web grows, our automated agents will need to show this kind of selectivity as well. But rather than over-use the word "context", perhaps a better term here would be "personalization". So each clients view of the GGG, the world wide graph of data, will be different, skewed towards the needs of that client. This will probably be done by a collaboration between new kinds of search agents and each client themselves.
