Analogy making in legal reasoning with neural networks and fuzzy logic

Analogy making from examples is a central task in intelligent system behavior. A lot of real world problems involve analogy making and generalization. Research investigates these questions by building computer models of human thinking concepts. These concepts can be divided into high level approaches as used in cognitive science and low level models as used in neural networks. Applications range over the spectrum of recognition, categorization and analogy reasoning. A major part of legal reasoning could be formally interpreted as an analogy making process. Because it is not the same as reasoning in mathematics or the physical sciences, it is necessary to use a method, which incorporates first the ability to specify likelihood and second the opportunity of including known court decisions. We use for modelling the analogy making process in legal reasoning neural networks and fuzzy systems. In the first part of the paper a neural network is described to identify precedents of immaterial damages. The second application presents a fuzzy system for determining the required waiting period after traffic accidents. Both examples demonstrate how to model reasoning in legal applications analogous to recent decisions: first, by learning a system with court decisions, and second, by analyzing, modelling and testing the decision making with a fuzzy system.     

Influence of whole-body X-ray irradiation and 2, 4, 6-triethyleneimino- 1, 3, 5-triazine on the hypnotic effect of hexobarbital in mice (author's transl)

The hypnotic effect of hexobarbital, measured in male NMRI-mice, was reduced during the first 24 hours after a unique or fractionated whole-body X-ray irradiation with 700 and 1400 R resp. 70 to 280 R and twelve hours after a unique intravenous injection of 16,0 mg tretamine per kilogram of body weight. Immediately after a unique injection of a high dose of tretamine and 24 hours after the last of four applications of 0,25 to 2,0 mg/kg which were repeated each after one day, the sleeping time after hexobarbital was prolonged. 24 hours after fractionated injection of tretamine (0,5 mg/kg each time), the concentration of hexobarbital in the brain showed a significant increase compared with the control animals. These results show that the decreased effect of hexobarbital in the early post-irradiation phase is based on pharmacodynamic processes while the increase of the effect produced by continued administration of tretamine can be explained by pharmaco-cinetic reasons.     

Loganic Acid Methyltransferase: Insights into the Specificity of Methylation on an Iridoid Glycoside

Loganin is an iridoid glycoside of interest as both an intermediate in the biosynthesis of indole alkaloids in plants and as a bioactive compound itself. Loganic acid methyltransferase catalyzes the methylation of a monoterpenoid glycoside precursor to produce loganin and demonstrates stereospecificity for the (6S,7R) substrate. We have biochemically characterized this biocatalyst and elucidated the basis for its strict substrate specificity. These studies could help facilitate the design of new classes of monoterpenoid indole alkaloids of pharmaceutical interest.     

Guilt and effortful control: Two mechanisms that prevent disruptive developmental trajectories.

Children’s guilt associated with transgressions and their capacity for effortful control are both powerful forces that inhibit disruptive conduct. The authors examined how guilt and effortful control, repeatedly observed from toddlerhood to preschool age, jointly predicted children’s disruptive outcomes in 2 multimethod, multitrait longitudinal studies (Ns = 57 and 99). Disruptive outcomes were rated by mothers at 73 months (Study 1) and mothers, fathers, and teachers at 52 and 67 months (Study 2). In both studies, guilt moderated effects of effortful control: For highly guilt-prone children, variations in effortful control were unrelated to future disruptive outcomes, but for children who were less guilt prone, effortful control predicted such outcomes. Guilt may inhibit transgressions through an automatic response due to negative arousal triggered by memories of past wrongdoing, regardless of child capacity for deliberate inhibition. Effortful control that engages a deliberate restraint may offset risk for disruptive conduct conferred by low guilt. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Representing Probabilistic Rules with Networks of Gaussian Basis Functions

There is great interest in understanding the intrinsic knowledge neural networks have acquired during training. Most work in this direction is focussed on the multi-layer perceptron architecture. The topic of this paper is networks of Gaussian basis functions which are used extensively as learning systems in neural computation. We show that networks of Gaussian basis functions can be generated from simple probabilistic rules. Also, if appropriate learning rules are used, probabilistic rules can be extracted from trained networks. We present methods for the reduction of network complexity with the goal of obtaining concise and meaningful rules. We show how prior knowledge can be refined or supplemented using data by employing either a Bayesian approach, by a weighted combination of knowledge bases, or by generating artificial training data representing the prior knowledge. We validate our approach using a standard statistical data set.