Constraint Lingo is a high-level language for specifying and solving tabular con-straint problems [FMT01]. We show the syntax of this language through ex-amples. Our software translates Constraint Lingo programs into a variety of

A method is presented to design instances of the puzzle minesweeper by ltering out those that are unlikely to be solvable without guesswork. To achieve this, the process of solving minesweeper is modeled as a constraint satisfaction problem, encoding inferences typically made by a player

to reason with natural language input. Finally, logic puzzles define precise and well-structured problems, which set clear evaluation criteria for the performance of the computational system trying to solve such puzzles. There is a variety of logic puzzles that one can use for such evaluation purposes

provide a simple computational model of human solving activity and evaluate it over collected data. Using the model we show that there are two sources of problem difficulty: complexity of individual steps (logic operations) and structure of dependency among steps. Beside providing a very good Sudoku

empirically. The sequential nature of the puzzle naturally conforms to a planning problem, hence we also present an experimental comparison with several leading AI planning systems. Other variants of the puzzle, such as ‘Fool’s Solitaire’ and ‘Long-hop’ Solitaire are also considered.

How can we predict the difficulty of a Sudoku puzzle? We give an overview of difficulty rating metrics and evaluate them on exten-sive dataset on human problem solving (more then 1700 Sudoku puzzles, hundreds of solvers). The best results are obtained using a computational model of human solving

How can we predict the difficulty of a Sudoku puzzle? We give an overview of difficulty rating metrics and evaluate them on exten-sive dataset on human problem solving (more then 1700 Sudoku puzzles, hundreds of solvers). The best results are obtained using a computa-tional model of human solving

financing constraints, namely to identify the investment opportunities and the constraints of a firm separately. The model also allows for other potential explanations of the observed phenomenon, in particular adjustment costs and uncertainty. I solve the model using dynamic programming methods and estimate

-tuples of units are compatible with which N-tuples of labels. We show that Latin square puzzles, finding N-ary relations, graph or auto-mata homomorphisms, graph colorings, as well as determining satisfiabil-ity of propositional logic statements and solving scene and edge labeling problems, are all special cases

) as additional explanatory variables, it does not solve the problem: the conditional relationship between investment and saving is assumed to be homogeneous as long as β i is common for all i. Secondly, the equation where q it denotes threshold variables, c a threshold parameter and where the 5 For a