Darwinian network

File:Darwinian Network vs Bayesian Network, 2015.png

A Darwinian network diagram that shows six populations, including

p (c, a b)

, short for

p (c, a, b)

, illustrated with a closed curve around the (clear) combative trait

b

and two (dark) docile traits

a

and

g

.

A Darwinian network (DN),^[1] proposed in 2015 by,^[2] is a probabilistic graphical model to simplify working with Bayesian networks.^[3]

Rather than modelling the variables in a problem domain, DNs represent the probability tables in the model. The graphical manipulation of the tables then takes on a biological feel, where a CPT $P (X | Y)$ is viewed as the novel representation of a population $p (C, D)$ using both combative traits $C$ (coloured clear) and docile traits $D$ (coloured dark).

DNs can unify modeling and reasoning tasks into a single platform. DNs can represent exact inference using either variable elimination^[4] or arc-reversal,^[5] lazy propagation,^[6] as well as how DNs can represent testing independencies. Adaptation and evolution are used to represent the testing of independencies and inference, respectively.

References

↑ http://www.darwiniannetworks.com/
↑ Butz, C. J.; Oliveira, J. S.; and dos Santos, A. E. 2015. Darwinian networks. In Proceedings of the Twenty-Eighth Canadian Artificial Intelligence Conference.
↑ Pearl, J. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann.
↑ Zhang, N.L., Poole, D.: A simple approach to Bayesian network computations. In: Tenth Canadian Artificial Intelligence Conference. pp. 171–178 (1994)
↑ Olmsted, S.: On Representing and Solving Decision Problems. Ph.D. thesis, Stanford University (1983)
↑ Madsen, A. L., and Jensen, F. V. 1999. Lazy propagation: A junction tree inference algorithm based on lazy evaluation. Artificial Intelligence 113(1-2):203–245.

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[1] ttp://www.darwiniannetworks.com/

[2] Butz, C. J.; Oliveira, J. S.; and dos Santos, A. E. 2015. Darwinian networks. In Proceedings of the Twenty-Eighth Canadian Artificial Intelligence Conference.

[3] Pearl, J. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann.

[4] Zhang, N.L., Poole, D.: A simple approach to Bayesian network computations. In: Tenth Canadian Artificial Intelligence Conference. pp. 171–178 (1994)

[5] Olmsted, S.: On Representing and Solving Decision Problems. Ph.D. thesis, Stanford University (1983)

[6] Madsen, A. L., and Jensen, F. V. 1999. Lazy propagation: A junction tree inference algorithm based on lazy evaluation. Artificial Intelligence 113(1-2):203–245.

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