Yes you can, a few years ago I made a simple CNN for a single Arabic phoneme classification. You can use spectogram or using MFCC / MFSC as features, as long all data has the same size (use padding or cropping if needed).
You may need RNN if you want to combine some phonemes to recognize a single word or longer.
I believe you may want to use a Sum Product Network for this task. SPNs are the state-of-the-art approach for face completion, and there are several more recent papers on this topic since the original above.
Importantly, the SPN paper also covers other approaches that work well for this task. If lower-resolution results are acceptable for your task, PCA ...
Initial state: initial position of the monkey.
climb on the crate,
get down the crate,
move the crate from one spot to another,
stack one crate on another,
walk from one spot to another,
grab bananas (if standing on the crate)
Goal test: did the monkey get the bananas?
Cost function: the number of actions completed
You are mixing up lots of things here. Specifically, you seem to be lacking a basic understanding of artificial neural networks and what they can do (e.g. what type of articifial neural networks are linear classifiers/regressors and which can model non-linear relationships).
Therefore, I'd take a step back and start with understanding the basics of AI. The ...
For example, from among house size, lot size, age of house and asking price, what formula best predicts selling price?
There is no general formula for this. Search for neural network regression and you can get started. The AI technique or any prediction algorithm in general will learn a function that maps from the input feature vector $(x_1, ...,x_n)$, ...
This depend on type of data you use.
Time sequence data
If the data advanced in time, a LSTM or similar RNN should be used. RNN calculate output through time. It works very good on time series data as it have a real sense of time. While CNN and MLP could work for time series data, it often don't work that well as different timestep of data is not defined. ...
I would go so far as to say that unless the training examples include predicate data- that is, data about conditions leading up to a crime or non-crime-, then you cannot have enough information to predict the occurence of a crime from conditions or events that happen in advance of a potential crime not yet committed.
It might be more informative to:
Label each combination of location, type, and time of crime with a crime rate. For example, theft, in Crystal City, at 11pm at night, occurs 20 times per year, or 0.4 times per resident per year.
Predict the crime rate, rather than individual events.
This avoids the need to have explicit examples of "non-crime", and lets ...
From the way you have phrased your question one can derive a couple of strong assumptions which simplify the problem tremendously and make it feasable:
We do not look for an agent being able to play the game but only an evaluation of settlement options (no other agents to be considered)
The evaluation of settlement options is static (i.e. does not change ...
Historically, the non-ML approach would be an expert system. This is typically a rules-based decision system, falling under the umbrella of symbolic AI.
These systems can have strong utility in limited contexts, but are generally "brittle" in that parameters not previously defined or accounted will produce no-compute or weak utility. Because the rules of ...
Catan is actually a much more complicated game than the simple rules would suggest, and an exact solution is probably beyond the scope of current AI techniques.
Monte Carlo Tree Search or Expectiminimax techniques seem like they could help, but are intended for games of perfect information. Catan is not a game of perfect information (the development cards ...