Europe has a new speedcubing champion, Germany's Phillip Weyer. Weyer solved a Rubik's cube puzzle in an average time of 7.88 seconds in the Rubik's Cube European Championship 2016. The 2016 Championship held in Prague, Czech Republic, is the seventh Rubik's Cube European Championship. It had 18 categories, including the main category of the classic 3x3 cube, solving the cube blindfolded or only using the feet. Weyer told Reuters that it took years of hard work to get to the top.

Geographic object-based image analysis (GEOBIA) framework has gained increasing interest recently. Following this popular paradigm, we propose a novel multiscale classification approach operating on a hierarchical image representation built from two images at different resolutions. They capture the same scene with different sensors and are naturally fused together through the hierarchical representation, where coarser levels are built from a Low Spatial Resolution (LSR) or Medium Spatial Resolution (MSR) image while finer levels are generated from a High Spatial Resolution (HSR) or Very High Spatial Resolution (VHSR) image. Such a representation allows one to benefit from the context information thanks to the coarser levels, and subregions spatial arrangement information thanks to the finer levels. Two dedicated structured kernels are then used to perform machine learning directly on the constructed hierarchical representation. This strategy overcomes the limits of conventional GEOBIA classification procedures that can handle only one or very few pre-selected scales. Experiments run on an urban classification task show that the proposed approach can highly improve the classification accuracy w.r.t.

" If you have any interest in AI or programming, this book is a good start.

Futurist Ray Kurzweil predicted in 1990 that a computer would beat a human world champion chess player by 1998. In 1997, that actually happened with IBM's Deep Blue. Since then, artificial intelligence (AI) has continued to advance rapidly, making now a good time to brush up on what is considered the next wave of highly disruptive technology. AI consists of many sub disciplines such as natural language processing, computer vision, knowledge representation and reasoning. The technology is making its way into a broad range of industries from marketing with behavioural targeting, to healthcare with accurate and early detection of complex diseases, to infrastructure with smarter urban planning.

This video is an introduction to Semusi's work and context aware systems. Semusi's work is at the intersection of big data, sensors and machine learning. We are building context aware mobile systems that will be aware of the context in which they operate and their behaviour will be based on the context derived from the soft and hard sensor inputs. For e.g. if we know the current context of a user is in-car we can give her location based ads that are in the radius of a mile or if we know the context is walking, we can give her location based ads that are in the range of a few hundred feet. In the smartphone era, Context is King!

Structure Of Intelligent Agents 12. Types of agent program 13. Goal based Agents 14. Utility-based agents 15.

It has been hypothesized that the brain organizes concepts into a mental map, allowing conceptual relationships to be navigated in a manner similar to that of space. Grid cells use a hexagonally symmetric code to organize spatial representations and are the likely source of a precise hexagonal symmetry in the functional magnetic resonance imaging signal. Humans navigating conceptual two-dimensional knowledge showed the same hexagonal signal in a set of brain regions markedly similar to those activated during spatial navigation. This gridlike signal is consistent across sessions acquired within an hour and more than a week apart. Our findings suggest that global relational codes may be used to organize nonspatial conceptual representations and that these codes may have a hexagonal gridlike pattern when conceptual knowledge is laid out in two continuous dimensions.

Bidirectional search algorithms interleave a search forward from the start state (start ) and a search backward (i.e. using reverse operators) from the goal state (goal). We say that the two searches “meet in the middle” if neither search expands a node whose g-value (in the given direction) exceeds C*/2 , where C* is the cost of an optimal solution. The only bidirectional heuristic search algorithm that is guaranteed to meet in the middle under all circumstances is the recently introduced MM algorithm (Holte et al. 2016). The feature of MM that provides this guarantee is its unique priority functions for nodes on its open lists. In this short note we present MMe, which enhances MM’s priority function and is expected to expand fewer nodes than MM under most circumstances. We sketch a proof of MMe’s correctness, describe conditions under which MMe will expand fewer nodes than MM and vice versa, and experimentally compare MMe and MM on the 10-Pancake problem.

Many real-world planning problems are best modeled as infinite search space problems, using numeric fluents. Unfortunately, most planners and planning heuristics do not directly support such fluents. We propose a search space abstraction technique that compiles a planning problem with numeric fluents into a finite state propositional planning problem. To account for the loss of precision resulting from the abstraction, we leverage a policy repair technique used for non-deterministic planning. We evaluate our approach on a set of benchmarks and compare it to state-of-the-art planners that deal with numeric fluents.

Real-time heuristic search models an autonomous agent solving a search task. The agent operates in a real-time setting by interleaving local planning, learning and move execution. In this paper we propose a simple parametric algorithm that combines weighting with learning from multiple neighbors. Doing so breaks heuristic admissibility but allows the agent to escape heuristic depressions more quickly. We prove completeness of the algorithm and empirically compare it to several competitors more than twenty years apart. In a large-scale evaluation the new algorithm found better solutions than the recent algorithms, despite not learning additional information that they do. Finally, we study robustness of the algorithms to noise in the heuristic function — a desirable property in a physical implementation of real-time heuristic search. The new algorithm outperforms its contemporaries.