This is a precursor to MDP planning:

• states: conjunction of “fluents” (which are state)
• actions: transition between fulents
• transitions: deleting of older, changed parts of fluents, adding new parts

Planning Domain Definition Language

A LISP used to specify a STRIPS-style planning problem.

Hierarchical Task Network

1. Decompose classical planning into a hierarchy of actions
2. Leverage High level actions to generate a coarse plan
3. Refine to smaller problems

Reading Notes

Strong Free Will vs. Weak Free Will — “will” and “bells inequality” is a demonstration of indeterminism/randomness between particles — but indeterminism and randomness a demonstration of will.

That if humans have free will, it should be spawened from the indeterminism of elementary particles

It asserts, roughly, that if indeed we humans have free will, then elementary particles already have their own small share of this valuable commodity.

proof by induction but assuming that all \(k < n\) is given.

We learn a Bayes Net grphical structure by following Bayes rule:

\begin{align} P(G|D) &\propto P(D|G) P(G) \\ &= P(G) \int P(D | \theta, G) P(\theta|G) d\theta \\ &= P(G) \prod_{i=1}^{n} \prod_{j=1}^{q_{i}} \frac{\Gamma(\alpha_{i,j,0})}{\Gamma(\alpha_{i,j,0} + m_{i,j,0})} \prod_{k=1}^{r_{i}} \frac{\Gamma(\alpha_{i,j,k} + m_{i,j,k})}{\Gamma(\alpha_{i,j,k})} \end{align}

where, we define: \(\alpha_{i,j,0} = \sum_{k} \alpha_{i,j,k}\).

The actual integration process is not provided, but mostly uninteresting. See Beta Distribution for a flavour of how it came about.

This is hard. We are multiply many gammas together, which is computationally lame. So instead, we use

Goal: using protein-protein interfaces and docking to learn about the polymerase behavior

Too bio-y and I’m literally not sure how to make of it