"The Crossword puzzle (CP) is a simple problem to illustrate the formalization process of a problem into a CSP. The problem is to place words of a dictionary in a given structure satisfying certain constraints. The variables are the rows and columns in the crossword, and their values are the words in a dictionary."
– Marc Torrens. An Application using the JCL: The Air Travel Planning System. Diploma Thesis, 1997, Chapter 1, Section 1.2.1.
DisCSP (Distributed Constraint Satisfaction Problem) is a general framework for solving distributed problems arising in Distributed Artificial Intelligence. A wide variety of problems in artificial intelligence are solved using the constraint satisfaction problem paradigm. However, there are several applications in multi-agent coordination that are of a distributed nature. In this type of application, the knowledge about the problem, that is, variables and constraints, may be logically or geographically distributed among physical distributed agents. This distribution is mainly due to privacy and/or security requirements.
Personally, my biggest initial stumbling block was this: The math used to implement regularization does not correspond to pictures commonly used to explain regularization. Take a look at the oft-copied picture (shown below left) from page 71 of ESL in the section on "Shrinkage Methods." Students see this multiple times in their careers but have trouble mapping that to the relatively straightforward mathematics used to regularize linear model training. The simple reason is that that illustration shows how we regularize models conceptually, with hard constraints, not how we actually implement regularization, with soft constraints! Regularization conceptually uses a hard constraint to prevent coefficients from getting too large (the cyan circles from the ESL picture).
Usually, you pop up in an exhibition, coming from vivid streets to the Silent Hall of art. The exhibition pops up where you are, suddenly, amidst your next Zoom call, or while you are checking your emails. And you are exposed to it. In these crazy pandemic times, they found a perfect way to present art, without put the visitors in danger to be Corona'ed: Plug-In. You install a plug-in to your browser, and every hour another artwork overfloods your PC windows.
In an attempt to automate industrial designing, researchers from Princeton University and Columbia University introduced a large dataset of 15 million two-dimensional real-world computer-aided designs -- SketchGraphs. Along with that to facilitate research in ML-aided design, they also launched an open-source data processing pipeline. Introduced during the International Conference on Machine Learning, SketchGraphs is aimed to train the artificial intelligence machine with this large dataset, in order to expertise it to assist humans in creating CAD models. In a recent paper, researchers revealed that each of the CAD sketches is represented with a geometric constraint graph and the understanding of the line and shape sequence in which the design was initially created. This will enable the predictions of what is going to be designed next.
Parametric computer-aided design (CAD) is the dominant paradigm in mechanical engineering for physical design. Distinguished by relational geometry, parametric CAD models begin as two-dimensional sketches consisting of geometric primitives (e.g., line segments, arcs) and explicit constraints between them (e.g., coincidence, perpendicularity) that form the basis for three-dimensional construction operations. Training machine learning models to reason about and synthesize parametric CAD designs has the potential to reduce design time and enable new design workflows. Additionally, parametric CAD designs can be viewed as instances of constraint programming and they offer a well-scoped test bed for exploring ideas in program synthesis and induction. To facilitate this research, we introduce SketchGraphs, a collection of 15 million sketches extracted from real-world CAD models coupled with an open-source data processing pipeline.
What do self-driving cars, face recognition, web search, industrial robots, missile guidance, and tumor detection have in common? They are all complex real world problems being solved with applications of intelligence (AI). This course will provide a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is applied to problems. You will learn about the history of AI, intelligent agents, state-space problem representations, uninformed and heuristic search, game playing, logical agents, and constraint satisfaction problems. Hands on experience will be gained by building a basic search agent.
During the virtually held Robotics: Science and Systems 2020 conference this week, scientists affiliated with the National University of Singapore (NUS) presented research that combines robotic vision and touch sensing with Intel-designed neuromorphic processors. The researchers claim the "electronic skin" -- dubbed Asynchronous Coded Electronic Skin (ACES) -- can detect touches more than 1,000 times faster than the human nervous system and identify the shape, texture, and hardness of objects within 10 milliseconds. At the same time, ACES is designed to be modular and highly robust to damage, ensuring it can continue functioning as long as at least one sensor remains. The human sense of touch is fine-grained enough to distinguish between surfaces that differ by only a single layer of molecules, yet the majority of today's autonomous robots operate solely via visual, spatial, and inertial processing techniques. Bringing humanlike touch to machines could significantly improve their utility and even lead to new use cases.
A team of researchers from the Chinese Academy of Sciences and the City University of Hong Kong has introduced a local-to-global approach that can generate lifelike human portraits from relatively rudimentary sketches. Recent deep image-to-image translation techniques have enabled the prompt generation of human face images from sketches, but these methods tend to suffer from overfitting to their inputs. They thus achieve the most realistic results only when the source drawings have high-quality artistry or are accompanied by edge maps. Unlike most deep learning based solutions for sketch-to-image translation that take input sketches as fixed, 'hard' constraints and then attempt to reconstruct the missing texture or shading information between strokes, the key idea behind the new approach is to implicitly learn a space of plausible face sketches from real face sketch images and find the point in this space that best approximates the input sketch. Because this approach treats input sketches more as'soft' constraints that will guide image synthesis, it is able to produce high-quality face images with increased plausibility even from rough and/or incomplete inputs.
There is a fundamental mismatch between the computational basis of spreadsheets and our knowledge of the real world. In spreadsheets, numeric data are represented as exact numbers and their mutual relations as functions, whose values (output) are computed from given argument values (input). However, in the real world, data are often inexact and uncertain in many ways, and the relationships, that is, constraints, between input and output are far more complicated. This article shows that interval constraint solving, an emerging AI-based technology, provides a more versatile and useful foundation for spreadsheets. The new computational basis is 100-percent downward compatible with the traditional spreadsheet paradigm.