What is DeepCausality?

DeepCausality is a hyper-geometric computational causality library that enables fast and deterministic context-aware causal reasoning over complex multi-stage causality models. Deep Causality adds only minimal overheads and thus is suitable for real-time applications without additional acceleration hardware.

What is Context?

Context means the setting of an event. You can think of context as all the information you need to know to truly understand something. DeepCausality enables context aware causality reasoning, meaning DeepCausality considers as much contextual information as you give it.

What is Causal State Machine?

A causal state machine encodes causal entities as types with the actual constraint represented as a causal function. One or more causal entities form a collection that is encapsulated in an abstraction called a causaloid. The causaloid represents the state, which then gets passed into a state machine for evaluation and subsequent action. Unlike a finite state machine, the causal state machine does not have to know all its causal entities upfront which allows for greater flexibility.

Where can I download DeepCausality?

How is DeepCausality licensed?

All software source code is licensed under the MIT License.

All documentation is distributed under the Creative Commons Attribution 4.0 International Licence.

All source code and documentation file are tagged with SPDX short-form identifiers to communicate FOSS license information in a simple, efficient, portable and machine-readable manner