# Soft Truth Values in the PSL model

This might sound like a trivial question. But since am starting out with my research in an area that is entirely new to me, I would really appreciate it if someone could kindly elucidate what Soft truth values mean.

I came across this in the context of PSL(Probabilistic Soft Logic). This paper stated that;

PSL uses first order logic rules as a template language for graphical models over random variables with soft truth values from the interval [0,1].

Any clarifications in this regard will be much appreciated.

In many such contexts, "truth" is defined as e.g., a value in $[0,1]$, and this is sometimes referred to as "soft truth values". The interpretation of such values can differ according to the usage. For example, a sentence with truth value $2/3$ can mean "the sentence is true with probability $2/3$" (i.e. it is true in $2/3$ of the models), or it could mean "the belief level I have in this sentence is $2/3$" (for some well defined concept of belief).
Or it could mean something like $2/3$ of the agents in this multiple agent system think the sentence is true (which is used to represent e.g. voting scenarios).