Abstract
This paper presents a generalized quantum model for describing purposes or goals of individual agents, and the way choices can be made that enable these goals to be achieved. The underlying model is a semantic vector space model, which is turned into a purposeful choice model by labelling some axes as objectives, and describing choices as transformations on the vector spaces that enable agents in the model to set these objective axes in sight.
We introduce this framework using a simplified example model of a dog trying to get food. Many parts of what has become the standard generalized quantum toolkit become apparent in this model, including learning, superposition, the importance of the metric used for normalization, classification, and a generalized uncertainty principle. The incorporation of purpose or goal into semantic vectors models also enables the analysis of traditional areas that are relatively new to artificial intelligence, including rhetoric, political science, and some of the philosophical questions touched by quantum theorists.
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Notes
- 1.
The claim that there is no loss at all to existing priorities when a new axis is considered is strictly true only in the continuous limit. For example, using the standard polar-coordinates parametrization where \(x = \cos (\theta )\) and \(y = \sin (\theta )\), when \(\theta = 0\), \(x = 1\), \(y = 0\), \(\frac{dy}{d\theta } = 1\) and \(\frac{dx}{d\theta } = 0\). In practice, we assume that all models will be quantized, and so to make an actual change, there will be some very small cost. The issues involved in quantizing vector models of information and cognition is not a focus of this paper: we note briefly that this example implies that it is advantageous for the smallest ‘representational quantum’ to be small.
- 2.
The difference between normalized coordinates of evenly-balanced vectors using Manhattan and Euclidean metrics is greatest in dimension 4. The proof is elementary, and consists of finding \(x \in [0, 1]\) such that \(f(x) = \sqrt{x} - x\) is maximized, so \(f'(x) = \frac{x^{-\frac{1}{2}}}{2} - 1 = 0\), implying \(x^\frac{1}{2} = \frac{1}{2}\) and so \(x = \frac{1}{4}\). We are not sure if this number has any special significance.
- 3.
Simulations demonstrate that a system with Euclidean normalization is more open to learning and, depending on the distribution of “food opportunities”, gets to eat more food in the long run. These results are however quite preliminary: please contact the author for more details.
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Widdows, D. (2014). Purposeful Choice and Point-of-View. In: Atmanspacher, H., Haven, E., Kitto, K., Raine, D. (eds) Quantum Interaction. QI 2013. Lecture Notes in Computer Science(), vol 8369. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54943-4_22
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