Perceptron Primitive Boolean Functions Thanks for reading.

I'm currently reading Tom Mitchell's Machine Learning (I'm a beginner into ML), and I'm on chapter 4 about perceptrons. I'm really confused about this paragraph:



I understand the AND but I'm still not so sure how OR can be represented. 

For example, if the inputs are:

$X_1 = +1$ 

$X_2 = -1$

In that case, using his logic for OR we would end up with the perceptron actually outputting -1, because:

$1*0.5  + -1*0.5 - .3 = -.3$

The function would give us a negative number. Is he wrong about the OR function? How would a single perceptron act as an OR?

Thank you!
I interpret it as the input takes binary value $0$ (false) and $1$ (true) while the output take $-1$ (false) and $1$ (true).

The map is $$sign\left(\sum_{i=1}^nw_ix_i+w_0\right).$$

If we let $w_i=0.5, i \ge 1$, then we have the mapping rule to be 

$$sign\left(\frac12\sum_{i=1}^nx_i+w_0\right).$$

For the AND operator, if we set $w_0=-0.8$, for it to take positive value both $x_i$ has to be $1$.

For the OR operator, if we set $w_0=-0.3$, we just need one of them to take positive value for it to be true.