Χωρική μνήμη και GOAP: μια ρεαλιστική προσέγγιση στην πλοήγηση πρακτόρων
Spatial memory and GOAP: a realistic approach to agent navigation

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Keywords
GOAPAbstract
Usually, when we apply shortest path algorithms, we simply want to find the
shortest path from the position of an agent to the position of a target. But this
is not exactly the case in the real world. What if the person has never visited
the area before? What if the available paths are obscured by obstacles like
buildings or trees?
Therefore, when a person is trying to reach a target by passing through an
area they have never visited before, they cannot know in advance the shortest
path to follow. In order to successfully navigate the area, they will most likely
start moving in a straight line heading in the general direction of his target until
they find an obstacle and then will try to avoid it by looking for a new path
around it. The more they explore the area the better they learn to navigate it
and eventually will be able to come to a conclusion about which is the shortest
path and whether or not they can even reach their target. This is the behavior I
tried to simulate with the application I created.
The agent's primary goal is to reach his girlfriend who is waiting for him to
begin their date, and he is already late! To reach her he has to cross a maze,
which, in addition to being complicated with many possible routes, it even has
colored doors that need keys of the same color to open. If the agent has
encountered a door while exploring and has exhausted all possible routes, then
he will try to open one of the doors by first searching for the right key. So, the
agent will have to explore the area and make a plan to finally reach his goal.