Abstract
The theoretical properties of active inference agents are impressive, but how do we realize effective agents in working hardware and software on edge devices? This is an interesting problem because the computational load for policy exploration explodes exponentially, while the computational resources are very limited for edge devices. In this paper, we discuss the necessary features for a software toolbox that supports a competent non-expert engineer to develop working active inference agents. We introduce a toolbox-in-progress that aims to accelerate the democratization of active inference agents in a similar way as TensorFlow propelled applications of deep learning technology.
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Notes
- 1.
For reference, we use the following abbreviations in this paper: Active Inference (AIF), Constrained Bethe Free Energy (CBFE), Expected Free Energy (EFE), (variational) Free Energy (FE), Free Energy Principle (FEP), Free Energy Minimization (FEM), Message Passing (MP), Reactive Message Passing (RMP).
- 2.
The computational load and complexity can only be equated in the absence of a Von Neumann bottleneck (i.e., with mortal computation or in-memory processing). This is because energy and time are ‘wasted’ by reading and writing to memory.
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Acknowledgments
I would like to acknowledge my colleagues at BIASlab (http://biaslab.org) for the stimulating work environment and the anonymous reviewers for excellent feedback on the draft version. Some wording in this document, such as footnote (see footnote 2), comes straight from a reviewer.
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de Vries, B. (2024). Toward Design of Synthetic Active Inference Agents by Mere Mortals. In: Buckley, C.L., et al. Active Inference. IWAI 2023. Communications in Computer and Information Science, vol 1915. Springer, Cham. https://doi.org/10.1007/978-3-031-47958-8_11
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