How do nodes recommend behavior? Does each node recommend one particular action at each time step? Does it recommend different actions to different degrees? What does the agent actually do—run an election in which each node votes on the action to take?
You’re asking a lot. It isn’t a cognitive architecture. It’s just modelling certain aspects of what we colloquially call “values”.
Without reference to a cognitive system, you can enumerate an agent’s preference systems, which would be described in terms of preferred and non-preferred outcomes. Then you observe the agent’s behavior over time, and categorize the outcome of each action, and consider all the preference systems that have preferences about that outcome, and count each case where a pair of preference systems had opposite preferences for that action. You don’t even have to record the outcome; you’re sampling the agent’s behavior only to get a good distribution of outcomes (rather than the flat distribution you would get by enumerating all possible outcomes).
If you have a particular cognitive architecture, you could map the nodes onto things you think are value propositions, and track the influence different nodes have on different action recommendations via some message-passing / credit-assignment algorithm. If you have a finite number of possible actions or action predicates, you could vary node values in a random (or systematic) way and estimate the correlation between each node and each action. That would restrict you to considering just the value in the propositional content of what I called preference systems.
You’re asking a lot. It isn’t a cognitive architecture. It’s just modelling certain aspects of what we colloquially call “values”.
Without reference to a cognitive system, you can enumerate an agent’s preference systems, which would be described in terms of preferred and non-preferred outcomes. Then you observe the agent’s behavior over time, and categorize the outcome of each action, and consider all the preference systems that have preferences about that outcome, and count each case where a pair of preference systems had opposite preferences for that action. You don’t even have to record the outcome; you’re sampling the agent’s behavior only to get a good distribution of outcomes (rather than the flat distribution you would get by enumerating all possible outcomes).
If you have a particular cognitive architecture, you could map the nodes onto things you think are value propositions, and track the influence different nodes have on different action recommendations via some message-passing / credit-assignment algorithm. If you have a finite number of possible actions or action predicates, you could vary node values in a random (or systematic) way and estimate the correlation between each node and each action. That would restrict you to considering just the value in the propositional content of what I called preference systems.
OK. Can you respond to my other question? Why should we care about this Internal Conflict thing, and why do we want to minimize it?