Probabilistic Architectures
Reasoning under uncertainty using Bayesian and graphical models
Probabilistic Architectures
AGI systems must operate in a noisy, uncertain world. Probabilistic architectures use the mathematical language of probability to represent and reason about this uncertainty.
Bayesian Networks
A way of representing a joint probability distribution over multiple variables using a directed acyclic graph (DAG).
- Nodes: Random variables.
- Edges: Conditional dependencies.
Inference
Estimating the state of some variables given observations of others (e.g., "Given sensory input , what is the probability that object is present?").
Markov Models
Architectures for dealing with sequences and time-series data.
- Hidden Markov Models (HMMs): Used in early speech and gesture recognition.
- Markov Random Fields: Undirected models used in computer vision.
Probabilistic Logic Networks (PLN)
An ambitious framework used in OpenCog/Hyperon that attempts to combine formal logic with probability.
- Truth Values: Instead of just 'True' or 'False', atoms have truth values representing strength and confidence.
Bayesian Brain Hypothesis
A theoretical framework in neuroscience suggesting that the brain is essentially a "Bayesian inference engine" that maintains a probabilistic model of the world and updates it based on sensory evidence.
Strengths
- Handling Noise: Naturally handles contradictory or incomplete data.
- Principled Learning: Bayesian updating provides a mathematically optimal way to integrate new evidence.
- Uncertainty Quantification: The system "knows what it doesn't know."
Limitations
- Computational Complexity: Exact inference is often NP-hard. Deep systems usually rely on approximations like Variational Inference or Markov Chain Monte Carlo (MCMC).
Next: Hyperon Overview