Compositional Neural Logic Programming

This paper introduces Compositional Neural Logic Programming (CNLP), a framework that integrates neural networks and logic programming for symbolic and sub-symbolic reasoning. We adopt the idea of compositional neural networks to represent first-order logic predicates and rules. A voting backward-forward chaining algorithm is proposed for inference with both symbolic and sub-symbolic variables in an argument-retrieval style. The framework is highly flexible in that it can be constructed incrementally with new knowledge, and it also supports batch reasoning in certain cases. In the experiments, we demonstrate the advantages of CNLP in discriminative tasks and generative tasks.

Decentralized Competition Produces Nonlinear Dynamics Akin to Klinotaxis

One of the biggest challenges in unravelling the complexity of living systems, is to fully understand the neural logic that translates sensory input into the highly nonlinear motor outputs that are observed when simple organisms crawl. Recent work has shown that organisms such as larvae that exhibit klinotaxis (i.e., orientation through lateral movements of portions of the body) can perform normal exploratory practices even in the absence of a brain. Abdominal and thoracic networks control the alternation between crawls and turns. This motivates the search for decentralized models of movement that can produce nonlinear outputs that resemble the experiments. Here, we present such a complex system model, in the form of a population of decentralized decision-making components (agents) whose aggregate activity resembles that observed in klinotaxis organisms. Despite the simplicity of each component, the complexity created by their collective feedback of information and actions akin to proportional navigation, drives the model organism towards a specific target. Our model organism’s nonlinear behaviors are consistent with empirically observed reorientation rate measures for Drosophila larvae as well as nematode C. elegans.