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Each problem takes a Boolean formula as input and the task is to compute the number of satisfying assignments. This can be further generalized by using larger domain sizes and attaching a weight to each satisfying assignment and computing the sum of these weights. It is known that any complex weighted #CSP problem is either in FP or #P-hard. [32]
Other considered kinds of constraints are on real or rational numbers; solving problems on these constraints is done via variable elimination or the simplex algorithm. Constraint satisfaction as a general problem originated in the field of artificial intelligence in the 1970s (see for example (Laurière 1978)).
The main problem of these algorithms is the possible presence of plateaus, which are regions of the space of assignments where no local move decreases cost. The second class of local search algorithm have been invented to solve this problem. They escape these plateaus by doing random moves, and are called randomized local search algorithms.
The current status of the CSP during the algorithm can be viewed as a directed graph, where the nodes are the variables of the problem, with edges or arcs between variables that are related by symmetric constraints, where each arc in the worklist represents a constraint that needs to be checked for consistency.
The randomness helps min-conflicts avoid local minima created by the greedy algorithm's initial assignment. In fact, Constraint Satisfaction Problems that respond best to a min-conflicts solution do well where a greedy algorithm almost solves the problem. Map coloring problems do poorly with Greedy Algorithm as well as Min-Conflicts. Sub areas ...
Symbolic AI is a top-down approach where the computer is given a set of rules—written by humans—and then must learn how to apply those rules to specific examples or circumstances.
Google’s blunder with images via the Gemini AI chatbot might portend much bigger problems of censorship and bias by Big Tech in the future, writes Rizwan Virk. Opinion: Google’s AI blunder ...
AI has plenty of technical shortcomings that we may be able to fix over time. But as we're learning, AI is also a reflection of the society that creates it—and that's a much harder fix.