I briefly skimmed through the McClellard chapter and it seems to mesh well with my understanding of probabilistic programming.
I think it would not go amiss to read Vikash Masinghka’s PhD thesis and the open-world generation paper to see a helpful probabilistic programming approach to these issues. In summary: we can use probabilistic programming to learn the models we need, use conditioning/query to condition the models on the constraints we intend to enforce, and then sample the resulting distributions to generate “actions” which are very likely to be “good enough” and very unlikely to be “bad”. We sample instead of inferring the maximum-a-posteriori action or expected action precisely because as part of the Bayesian modelling process we assume that the peak of our probability density does not necessary correspond to an in-the-world optimum.
I agree that choosing an action randomly (with higher probability for good actions) is a good way to create a fuzzy satisficer. Do you have any insights into how to:
create queries for planning that don’t suffer from “wishful thinking”, with or without nested queries. Basically the problem is that if I want an action conditioned on receiving a high utility (e.g. we have a factor on the expected utility node U equal to e^(alpha * U) ), then we are likely to choose high-variance actions while inferring that the rest of the model works out such that these actions return high utilities
extend this to sequential planning without nested nested nested nested nested nested queries
I think it would not go amiss to read Vikash Masinghka’s PhD thesis and the open-world generation paper to see a helpful probabilistic programming approach to these issues. In summary: we can use probabilistic programming to learn the models we need, use conditioning/
queryto condition the models on the constraints we intend to enforce, and then sample the resulting distributions to generate “actions” which are very likely to be “good enough” and very unlikely to be “bad”. We sample instead of inferring the maximum-a-posteriori action or expected action precisely because as part of the Bayesian modelling process we assume that the peak of our probability density does not necessary correspond to an in-the-world optimum.I agree that choosing an action randomly (with higher probability for good actions) is a good way to create a fuzzy satisficer. Do you have any insights into how to:
create queries for planning that don’t suffer from “wishful thinking”, with or without nested queries. Basically the problem is that if I want an action conditioned on receiving a high utility (e.g. we have a factor on the expected utility node U equal to e^(alpha * U) ), then we are likely to choose high-variance actions while inferring that the rest of the model works out such that these actions return high utilities
extend this to sequential planning without nested nested nested nested nested nested queries