We present a connectionist approach for solving Tangram puzzles. Tangram is an ancient Chinese puzzle where the object is to decompose a given figure into seven basic geometric figures. One connectionist approach models Tangram pieces and their possible placements and orientations as connectionist neuron units which receive excitatory connections from input units defining the puzzle and lateral...

This workshop paper addresses Heuristic approaches to solving Sudoku puzzles, with particular focus on Particle Swarm Optimisation(PSO). Sudoku problems and their constraints will be discussed. Heuristics used to solve Sudoku will be identified. We will then propose a component framework for supporting PSO and other Heuristics. Doing so allows us to separately detail aspects of PSO such as init...

The consequence relation and perfect usage of derivation is a necessary knowledge for layers, economists, engineers and for hundreds of other different professionals. Students in the low and mid level education system are able to solve simple logical puzzles without any special training, but some of the puzzles in Smullyan’s books present a challenge even for university students. Some of the lo...

The GoogleTM API enables software agents to query and use search results from the large collections of data available via the ever-popular Google search engine. Web searches using Google are exposed to over 4 billion pages, many of which are cached within Google. While the Google API may be used to produce customized user interfaces to Google, the API also provides direct programmatic access to...

Crossword puzzle solving is a classic constraint satisfaction problem, but, when solving a real puzzle, the mapping from clues to variable domains is not perfectly crisp. At best, clues induce a probability distribution over viable targets, which must somehow be respected along with the constraints of the puzzle. Motivated by this type of problem, we describe a formal model of constraint satisf...

We present a novel algorithm based on “loop constraints” for assembling non-overlapping square-piece jigsaw puzzles where the rotation and the position of each piece are unknown. Our algorithm finds small loops of puzzle pieces which form consistent cycles. These small loops are in turn aggregated into higher order “loops of loops” in a bottom-up fashion. In contrast to previous puzzle solvers ...

There has been a growing interest in image jigsaw puzzles with square shaped pieces. A solver takes as input square shaped patches of the same size belonging to an image and attempts to reconstruct the image. The key components of a jigsaw solver are a compatibility metric and an assembly algorithm. A compatibility metric uses the color content of the image patches to identify which pairs of pi...

We propose a novel Linear Program (LP) based formulation for solving jigsaw puzzles. We formulate jigsaw solving as a set of successive global convex relaxations of the standard NP-hard formulation, that can describe both jigsaws with pieces of unknown position and puzzles of unknown position and orientation. The main contribution and strength of our approach comes from the LP assembly strategy...

The aim of the sudoku puzzle (also known as number place in the United States) is to enter a numeral from 1 through 9 in each cell of a grid, most frequently a 9× 9 grid made up of 3× 3 subgrids, starting with various numerals given in some of the cells (the “givens”). Each row, column, and region must contain only one instance of each numeral. In this paper we show how a sudoku puzzle can be s...

We study solvers of nonogram puzzles, which are good examples of constraint-satisfaction problems. Given an optimal solving module for solving a given line, we compare performance of three algorithmic solvers used to select the order in which to solve lines with a reinforcement-learningbased solver. The reinforcement-learning (RL) solver uses a measure of reduction of distance to goal as a rewa...