artificial-intelligence

问题 I quote from Artificial Intelligence: A Modern Approach: The properties of depth-first search depend strongly on whether the graph-search or tree-search version is used. The graph-search version, which avoids repeated states and redundant paths, is complete in finite state spaces because it will eventually expand every node. The tree-search version, on the other hand, is not complete [...]. Depth-first tree search can be modified at no extra memory cost so that it checks new states against

问题 I am trying to find how the C4.5 algorithm determines the threshold value for numeric attributes. I have researched and can not understand, in most places I've found this information: The training samples are first sorted on the values of the attribute Y being considered. There are only a finite number of these values, so let us denote them in sorted order as {v1,v2, …,vm}. Any threshold value lying between vi and vi+1 will have the same effect of dividing the cases into those whose value of

问题 I have a question regarding Genetic Programming. I am going to work on a genetic algorithm for a game called Battleships. My question is: How would I decide upon a "decision" model for the AI to evolve? And how does that work? I have read multiple papers and multiple answers that just speak about using different models, but could not find something specific, which, unfortunately, I apparently need to wrap my head around the problem. I want it to evolve over multiple iterations and "learn"

问题 I recently started playing Flow Free Game. Connect matching colors with pipe to create a flow. Pair all colors, and cover the entire board to solve each puzzle in Flow Free. But watch out, pipes will break if they cross or overlap! I realized it is just path finding game between given pair of points with conditions that no two paths overlap. I was interested in writing a solution for the game but don't know where to start. I thought of using backtracking but for very large board sizes it will

问题 I recently started playing Flow Free Game. Connect matching colors with pipe to create a flow. Pair all colors, and cover the entire board to solve each puzzle in Flow Free. But watch out, pipes will break if they cross or overlap! I realized it is just path finding game between given pair of points with conditions that no two paths overlap. I was interested in writing a solution for the game but don't know where to start. I thought of using backtracking but for very large board sizes it will

问题 As it currently stands, this question is not a good fit for our Q&A format. We expect answers to be supported by facts, references, or expertise, but this question will likely solicit debate, arguments, polling, or extended discussion. If you feel that this question can be improved and possibly reopened, visit the help center for guidance. Closed 7 years ago . Is there a rule of thumb (or set of examples) to determine when to use genetic algorithms as opposed to neural networks (and vice

问题 As it currently stands, this question is not a good fit for our Q&A format. We expect answers to be supported by facts, references, or expertise, but this question will likely solicit debate, arguments, polling, or extended discussion. If you feel that this question can be improved and possibly reopened, visit the help center for guidance. Closed 7 years ago . Is there a rule of thumb (or set of examples) to determine when to use genetic algorithms as opposed to neural networks (and vice

问题 Although I know that SARSA is on-policy while Q-learning is off-policy, when looking at their formulas it's hard (to me) to see any difference between these two algorithms. According to the book Reinforcement Learning: An Introduction (by Sutton and Barto). In the SARSA algorithm, given a policy, the corresponding action-value function Q (in the state s and action a, at timestep t), i.e. Q(s t , a t ), can be updated as follows Q(s t , a t ) = Q(s t , a t ) + α*(r t + γ*Q(s t+1 , a t+1 ) - Q

问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed last year . Currently I'm learning about neural networks and I'm trying to create an application that can be trained to recognize handwritten characters. For this problem I use a feed-forward neural network and it seems to work when I train it to recognize 1, 2 or 3 different characters. But

问题 In terms of artificial intelligence and machine learning, what is the difference between supervised and unsupervised learning? Can you provide a basic, easy explanation with an example? 回答1: Since you ask this very basic question, it looks like it's worth specifying what Machine Learning itself is. Machine Learning is a class of algorithms which is data-driven, i.e. unlike "normal" algorithms it is the data that "tells" what the "good answer" is. Example: a hypothetical non-machine learning