"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).
EAs are useful for exploring a large optimisation space where it is infeasible to just enumerate all possible solutions. This is because an EA can often converge to the most promising area in the optimisation space quicker than a general search heuristic. The EA is also shown to be faster than a dynamic programming based search  in finding the optimal transformation for the Fast Fourier Transformation (FFT) . When compared to supervised learning, EAs have the advantage of requiring little problem specific knowledge, and hence that they can be applied on a broad range of problems. However, because an EA typically relies on the empirical evidences (e.g.
This story was originally posted by Digital Trends. Whether it's beating us at games like the board game Go or stealing our jobs, the killer combination of artificial intelligence and robots are owning us puny humans left and right. The latest example of a high-tech achievement that will make you feel on the verge of extinction? A robot that's capable of completing a Rubik's Cube puzzle in just 0.38 seconds flat -- which includes image capture and computation time, along with physically moving the cube. Not only is that significantly faster than the human world record of 4.59 seconds, but it's also a big improvement on the official robot world record of 0.637 seconds, as set in late 2016.
Most of us can't solve a Rubik's Cube to save a life, let alone finish the puzzle in seconds. Australian speedcuber Feliks Zemdegs certainly makes it look easy, especially when he broke the Rubik's Cube world record (again) at the Cube for Cambodia competition in Melbourne, Australia on Sunday. Zemdegs, who had previously held the world record, managed to solve a Rubik's Cube at a frighteningly quick 4.22 seconds, beating a record of 4.59 which was both held by Feliks and South Korea's SeungBeom Cho. Correction: A previous version of this article stated that Patrick Ponce was the previous record holder, when it was both Feliks and Cho who simultaneously held the record. 'Arrested Development' Season 5 trailer is here and we can taste the happy'Luke Cage' Season 2 trailer introduces quite the villain YouTuber takes the music out of Justin Timberlake's'Can't Stop the Feeling' video, and it's pretty weird
An Australian man has set a new world record for fastest time to solve a Rubik's cube at just 4.22 seconds. Feliks Zemdegs is a 22-year-old'speedcuber' from Australia who participated in the Cube for Cambodia 2018 event on Saturday in Melbourne. He broke the previous world record of 4.59 seconds by solving a 3x3x3 cube in just 4.22 seconds. Feliks Zemdegs set a world record for fastest time to solve a Rubik's cube at just 4.22 seconds The 22-year-old from Australia broke the previous record at the Cube for Cambodia 2018 event on Saturday in Melbourne. A video captured his record-breaking performance as he sat alongside other speedcubers of all ages.
In the context of tree-search stochastic planning algorithms where a generative model is available, we consider on-line planning algorithms building trees in order to recommend an action. We investigate the question of avoiding re-planning in subsequent decision steps by directly using sub-trees as action recommender. Firstly, we propose a method for open loop control via a new algorithm taking the decision of re-planning or not at each time step based on an analysis of the statistics of the sub-tree. Secondly, we show that the probability of selecting a suboptimal action at any depth of the tree can be upper bounded and converges towards zero. Moreover, this upper bound decays in a logarithmic way between subsequent depths. This leads to a distinction between node-wise optimality and state-wise optimality. Finally, we empirically demonstrate that our method achieves a compromise between loss of performance and computational gain.
A problem that often arises in using abstraction is the generation of abstract states, called spurious states, that are--in the abstract space--reachable from some abstract image of a state s, but which have no corresponding state in the original space reachable from s. Spurious states can have a negative effect on pattern database sizes and heuristic quality. We formally define a property--the downward path preserving property (DPP)--that guarantees an abstraction has no spurious states. Analyzing the computational complexity of (i) testing the DPP property for a given state space and abstraction and of (ii) determining whether this property is achievable at all for a given state space, results in strong hardness theorems. On the positive side, we identify formal conditions under which finding DPP abstractions is tractable.
We study novel approaches for solving of hard combinatorial problems by translation to Boolean Satisfiability (SAT). Our focus is on combinatorial problems that can be represented as a permutation of n objects, subject to additional constraints. In the case of the Hamiltonian Cycle Problem (HCP), these constraints are that two adjacent nodes in a permutation should also be neighbors in the original graph for which we search for a Hamiltonian cycle. We use the absolute SAT encoding of permutations, where for each of the n objects and each of its possible positions in a permutation, a predicate is defined to indicate whether the object is placed in that position. For implementation of this predicate, we compare the direct and logarithmic encodings that have been used previously, against 16 hierarchical parameterizable encodings of which we explore 416 instantiations. We propose the use of enumerative adjacency constraints--that enumerate the possible neighbors of a node in a permutation-- instead of, or in addition to the exclusivity adjacency constraints--that exclude impossible neighbors, and that have been applied previously. We study 11 heuristics for efficiently choosing the first node in the Hamiltonian cycle, as well as 8 heuristics for static CNF variable ordering. We achieve at least 4 orders of magnitude average speedup on HCP benchmarks from the phase transition region, relative to the previously used encodings for solving of HCPs via SAT, such that the speedup is increasing with the size of the graphs.
True distance memory-based heuristics (TDHs) were recently introduced as a way to obtain admissible heuristics for explicit state spaces. In this paper, we introduce a new TDH, the portal-based heuristic. The domain is partitioned into regions and portals between regions are identified. True distances between all pairs of portals are stored and used to obtain admissible heuristics throughout the search. We introduce an A*- based algorithm that takes advantage of the special properties of the new heuristic. We study the advantages and limitations of the new heuristic. Our experimental results show large performance improvements over previously-reported TDHs for commonly used classes of maps.
The field of artificial intelligence needs to attract new researchers to the field to continue current explorations and look for novel approaches to tomorrow's problems. One approach involves providing students with learning tools that excite their imagination and help them obtain an appreciation for what artificial intelligence can do. The tools described here are used in an undergraduate course at Sam Houston State University. They include heuristic-driven search in a potential game's terrain map, reinforcement learning in a tank battle game, and game tree search techniques in tictac-toe.
A common direction in heuristic search is to develop techniques for very large combinatorial domains (e.g., permutation puzzles) where the state space is defined only implicitly, due to its exponential size. However, there are many domains, such as map-based searches (common in GPS navigation, computer games, and robotics) where the entire state-space is given explicitly. Optimal paths for such domains can be found relatively quickly with simple heuristics, especially when compared to the time it takes to explore exponentially large combinatorial problems. Relative quickness, however, might still not be fast enough in certain real-time applications, where further improvement towards high-speed performance is especially valued. We present an approach that relies on preprocessing techniques that can dramatically reduce search costs, and do not compromise search optimality.