As you say, GPS is not precise enough for the purpose (until recently it was only accurate within 5m or so, since 2018 there are receivers that have an accuracy of about 30cm). Instead, autonomous vehicles have a multitude of sensors, mostly cameras and radar, which record the surrounding area and monitor the road ahead. Due to them being flat, mostly one ...
I would start with:
PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization
VidLoc: A Deep Spatio-Temporal Model for 6-DoF Video-Clip Relocalization
Image-based localization using LSTMs for structured feature correlation
Image-based Localization using Hourglass Networks
UAN: Unified Attention Network for Convolutional Neural Networks
You can implement a Reinforcement Learning agent with the following aspects:
An action of adding or removing a question of a certain category, e.g.
Add a grammar question, remove a grammar question, add vocabulary question, etc.
Using the Flow Model, you can build a vector of flow features per topic. such that each element in ...
A simple decision tree would be suitable, and represents one of the easiest forms of AI. Don't over-complicate simple problems.
The simplest approach would likely run faster, and require less system resources, so, unless this project is designed as an entree into machine learning, that approach is way overkill for this simple problem.
You could build a MLC(machine learning classifier) based on her/his answers and apply that to a large database of questions to see which type of questions will probably yield an incorrect answer.
And then provide those questions (Assuming one would learn more from working at her/his weaknesses) to provide a steeper learning curve, continuously adapting ...
even though that's not really AI, the easiest way to do that would probably be to put coefficients on each question
e.g. your question would have something like
the lower the level, the less questions of this type will appear
at the end of the level, you sum each question times how ...
If I had to implement a path exploration/finding algorithm on a robot, I would follow these steps:
Make sure you can detect your position. You need to be able to record your position otherwise you have no reference for the exploration. You don't need a global positioning system (like GPS), a local one is more than enough in your case. This means that the ...
Since you mention the A* algorithm, then you are definitely using a heuristic somewhere in there, at least with the A* algorithm while solving the subproblems using the straight-line distance as your heuristic function.
Although your approach does not seem to incorporate a "shortcut mathematical formula" as a heuristic after that, it does us a precalculated ...
A 'heuristic' is simply a 'rule-of-thumb', i.e. something which doesn't guarantee an optimal solution to a problem.
Beyond the above notion (certainly within the discipline of optimization), the notion of what constitutes a heuristic is not particularly strict, and could certainly include hints for constructing a new solution from parts of a previous one, ...
Whether or not a label fits any particular instance depends on what you're using the label for. If something specific is riding on whether this approach is a 'heuristic' or not, that context is important.
But I wouldn't call this a heuristic, because I think of that as a shortcut for solving a problem, not either storing a solution or reformulating the ...
An agent perceives the environment through sensors and act according to the incoming percepts (agent's perceptual input at any instant). An autonomous vacuum cleaner can be as simple as
(blocki, clean) --> Move to blocki+1
(blocki, dirty) --> Clean
This is just a general description, actual one is more complicated. Or the bot can have a memory where it ...