A situation can be modeled as a set of related entities together with a set
of constraints. Think of it as a partially determined simulation or
description whose scope can be expanded as reasoning or analysis proceeds.
(Note that this expansion of scope need not be restricted to a particular
temporal direction.) Another way to look at it is as a collection of
phenomenal binding sites (like noun phrases, or fields in the SELECT
portion of a SQL query) and relationships between those binding sites
(analogous to verb clauses & prepositional relations, or conditions in the
WHERE portion of a SQL query). As such, a situation can be seen as a
compound pattern or rule, which can potentially be matched against
sequences of states of the environment. One situation can be considered a
generalization of another if it can match all of the environmental state
sequences that can be matched by the other, while preserving the mapping
from entities to components of the environmental state sequence that
allowed the other situation to match. The most general or natural way to
represent a situation, I think, would be as a graph in which vertices are
labeled with descriptions of entities and edges are labeled with
relationships those entities must abide by.

Use of the term "irrelevant" towards situations assumes a purpose or goal
to which a situation may be relevant. To determine whether a situation is
relevant with respect to a particular goal, several conditions must be met
by the situation. It must be possible to reach the situation from the
current situation with non-trivial probability and tolerable cost, and the
perceived distance or cost to the goal must be reduced or maintained. The
A* algorithm comes to mind, but in this case it would be operating on a
meta-graph -- with situation graphs as vertices -- whose topology is
dynamically generated by applying transformational rules to generate
candidate future situations from current ones. For example, supposing the
goal is to enjoy an apple, and the situation is that you have an apple
which is dirty, three of the many transformations which could be applied
are:

   - Eat the apple immediately. This transformation results in a relevant
   situation due to the reduced distance to goal, despite increased cost from
   reduced enjoyment.
   - Wash the apple. This transformation results in a relevant situation
   due to the reduced cost to the goal, despite increased goal distance due to
   the delay.
   - Throw the apple at the wall. This transformation neither reduces cost
   nor distance to the goal, and thus produces an irrelevant situation.

In each case, the new situation must be constructed by applying a graph
transformation associated with the proposed action before it can be
determined if it is truly relevant. By recording the
situation/action/success triples from previous experience, and searching
for such triples that maximize the intersection (i.e. similarity) between
initial situations and the one that we are currently facing, it should be
possible to heuristically estimate the utility of the given action within
the current situation, allowing us to sort actions according to likely
utility and thereby avoid processing of irrelevant actions and situations
until better options have already been exhausted.

On Mon, Apr 28, 2014 at 1:35 AM, Piaget Modeler <[email protected]> wrote:

> How do we form *situations *in our mind?
>
> Some may be actual, hypothetical, or anticipatory.
>
> How would you model situations?
>
> Assuming that we have millions of them to choose from, how
> do we ignore irrelevant situations and work with relevant ones?
>
> I have some theories, but I'd like to hear your thoughts?
>
> ~PM
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