Multi-Agent Simulator Program

Hi All,
I'm working on a multi-agent simulator in which agents learn an environment while moving through it. Being somewhat new to Java, I'm looking for suggestions on what data structure to use for storing an agents associative memory. To give you an idea, I'm currently using an array for the agent's exploratory memory (a buffer), then when the agent finds food the path used to find food is then copied to the agent's associative memory and the exploratory memory is whiped clean. The problem I'm encountering is I don't want just a bunch of paths to be stored in the agent's associative memory - exsecially paths that have exact subpaths. For example, if an agent moves North, North, East, then South, and finds food, that path will be stored in associative memory - in case the agent finds itself on that same path it can use it's associative memory to find food. The problem is what if the agent does find itself on that same path, but moved South then West prior to finding the above path. My current logic updates associative memory with the full path taken to reach food. Therefore I would have two paths in my associative memory but one path would be a subpath of another. I guess I'm looking for a data structure that can store full paths as well as parts of paths that connect to full paths. Also, the agents don't know where the food is, they only know paths to food. I can't make a matrix for the agent mapping the environment. I was considering storing full paths and using an emotional context, but wouldn't that be inefficient to store all full paths when some paths are subpaths of other paths?
Any suggestions?
Thanks

The agents must store paths to food. Right now the agents move acorrding to their schema. If they get stuck a random set of moves is generated for that agent. The idea of the simulation is for agents to learn the environment (paths to food) through the use of their inborn schema and associative memory. For example, when an agent finds food, that path is stored in it's associative memory. The agent is then placed in a random location in the environment. That agent will then use its schema and associative memory to find food, meaning when the agent moves it will check it's associative memory for a move that matches the current move - if no match is found it will move according to its inborn schema (default move pattern).

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