2019年7月6日 · In English, the fringe is (also) defined as the outer, marginal, or extreme part of an area, group, or sphere of activity. In the context of AI search algorithms, the state (or search) space is usually represented as a graph, where nodes are states and the edges are the connections (or actions) between the corresponding states.

2019年8月30日 · What are the differences between the A* algorithm and the greedy best-first search algorithm? Which one should I use? Which algorithm is the better one, and why?

2018年4月13日 · A heuristic is admissible if it never overestimates the true cost to reach the goal node from n n. If a heuristic is consistent, then the heuristic value of n n is never greater than the cost of its successor, n′ n ′, plus the successor's heuristic value. Why is A*, using tree or graph searches, optimal, if it uses an admissible heuristic?

An agent aims to find a path on a hexagonal map, with an initial state s0 s 0 in the center and goal state s∗ s ∗ at the bottom as depicted below. The map is parametrized by the distance n ≥ 1 n ≥ 1 from s0 s 0 to any of the border cells (n = 3 n = 3 in the depicted example). The agent can move from its current cell to any of the 6 adjacent cells. How can we find the number of node ...

2019年11月24日 · The evaluation function is used to choose the next node to visit from the fringe, which is the set of nodes that can potentially be visited. Whenever we visit a node, we remove it from the fringe.

2018年8月14日 · What I described above would be the standard way to handle this problem. Intuitively, I suspect a different "solution" could be to always randomize the order of a new list of successor states before adding them to the frontier. This way, you do not avoid the problem of occasionally adding states that you've already previousl expanded to the frontier, but I do think …

There is always a lot of confusion about this concept, because the naming is misleading, given that both tree and graph searches produce a tree (from which you can derive a path) while exploring the search space, which is usually represented as a graph. Differences Firstly, we have to understand that the underlying problem (or search space) is almost always represented as …

2020年6月12日 · Fully convolution networks A fully convolution network (FCN) is a neural network that only performs convolution (and subsampling or upsampling) operations. Equivalently, an FCN is a CNN without fully connected layers. Convolution neural networks The typical convolution neural network (CNN) is not fully convolutional because it often contains fully connected layers …

2019年6月27日 · I don't understand the proof that A∗ A ∗ is optimal. The proof is by contradiction: Assume A∗ A ∗ returns p p but there exists a p′ p ′ that is cheaper. When p p is chosen from the frontier, assume p′′ p ″ (Which is part of the path p′ p ′) is chosen from the frontier. Since p p was chosen before p′′ p ″, then we have cost(p) + heuristic(p) ≤ cost(p ...

2021年10月14日 · This should have been a comment (for the most part). You should have encouraged the asker to change his question to something like: when should I use genetic algorithms as opposed to PSO?