"Search is a problem-solving technique that systematically explores a space of problem states, i.e., successive and alternative stages in the problem-solving process. Examples of problem states might include the different board configurations in a game or intermediate steps in a reasoning process. This space of alternative solutions is then searched to find an answer. Newell and Simon (1976) have argued that this is the essential basis of human problem solving. Indeed, when a chess player examines the effects of different moves or a doctor considers a number of alternative diagnoses, they are searching among alternatives."
– from Section 1.2 of Chapter One of George F. Luger's textbook, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 5th Edition (Addison-Wesley; 2005).
Humanoid robots walking across intermittent terrain, robotic arms grasping multifaceted objects, or UAVs darting left or right around a tree ... many of the dynamics and control problems we face today have both rich nonlinear dynamics and an inherently combinatorial structure. In this talk, Tedrake will review some recent work on planning and control methods which address these two challenges simultaneously.
Plenty of efficient algorithms exist to solve a rubik's cube. I was curious to find out if a neural net could learn how to solve a cube in the most "efficient" way, by solving the cube in less than 20 moves, i.e god's number. I used a 2 layer neural net: 1 convnet layer and 1 feedforward layer. For the training set, I generated games at random during training for games of 10 moves or less from solved with the corresponding solutions as label.
I gave myself 24 hours using nothing but online tutorials with the hopes of solving a Rubik's cube. Comment below and let us know what you want us to learn on the next episode of In A Day! Subscribe to Watercool and watch more videos here. 'The Blacklist' Season 5 has all the father-daughter drama we've been waiting for
In this blog, we'll take a deep dive into Spark's Cost Based Optimizer (CBO) and discuss how Spark collects and stores these statistics, optimizes queries, and show its performance impact on TPC-DS benchmark queries. Spark implements this query using a hash join by choosing the smaller join relation as the build side (to build a hash table) and the larger relation as the probe side 1. With the correct size/cardinality information for both sides, Spark 2.2 would choose the left side as the build side resulting in significant query speedups. In order to improve the quality of query execution plans, we enhanced the Spark SQL optimizer with detailed statistics information.
The negative gradient tells us that there is an inverse relationship between mpg and displacement with one unit increase in displacement resulting in a 0.04 unit decrease in mpg. How are these intercept and gradient values calculated one may ask? Each set of xy data points are iterated over to find the squared error, all squared errors are summed and the sum is divided by n to get the MSE. Next, we can calculate the MSE by summing the squared differences between observed yvalues and our predicted y values then dividing by the number of observations n. This gives a MSE of 9.911209 for this linear model.
More random searches, a savings consultant and Dallas' worst elementary school: What's new in education L.A. Unified is pushing principals to meet district requirements for using random searches and metal detector scans to find students' weapons. L.A. Unified is pushing principals to meet district requirements for using random searches and metal detector scans to find students' weapons. A new report found that California's rural school districts don't have access to enough teacher professional development resources to ensure a smooth implementation of the Common Core. A new report found that California's rural school districts don't have access to enough teacher professional development resources to ensure a smooth implementation of the Common Core.
I'd been searching for her online under variations of the name Maria Christina Sugatan since we lost touch in 1997, after our mom refused to let me speak to her. But the years ticked by, and in my mind she finished high school, started college, and got a job. As part of the last generation to grow up without the internet, I am still not accustomed to the drastic ways search algorithms can direct people's lives. Because of that twist of fate--and because then Facebook and Google didn't recognize Krissy as a variation of Chrissy (Facebook still doesn't)--I had no idea she had to drop out of school during the fall semester of her senior year, when Mama suddenly lost her apartment and our whole family moved into one room at a motel.
A recent study shows that the question of whether a scrambled Rubik's cube of any size can be solved in a given number of moves is what's called NP-complete – that's maths lingo for a problem even mathematicians find hard to solve. To prove that the problem is NP-complete, Massachusetts Institute of Technology researchers Erik Demaine, Sarah Eisenstat, and Mikhail Rudoy showed that figuring out how to solve a Rubik's cube with any number of squares on a side in the smallest number of moves will also give you a solution to another problem known to be NP-complete: the Hamiltonian path problem. On the other hand, problems that have algorithms that run their course in a more reasonable amount of time based on the number of inputs are called P. Researchers are still unsure whether algorithms exist that can solve NP-complete problems faster. "We know an algorithm to solve all cubes in a reasonable amount of time," Demaine says.
The effectiveness of the minimax algorithm is heavily based on the search depth we can achieve. This helps us evaluate the minimax search tree much deeper, while using the same resources. The alpha-beta pruning is based on the situation where we can stop evaluating a part of the search tree if we find a move that leads to a worse situation than a previously discovered move. It's a helpful resource for exploring beyond these basic concepts I introduced here.