Comparative Research is Harder Than We Thought: Regional Differences in Experts’ Understanding of Electoral Integrity Questions

Super Mario Bros. is Harder/Easier Than We Thought

Mario is back! In this sequel, we prove that solving a generalized level of Super Mario Bros. is PSPACE-complete, strengthening the previous NP-hardness result (FUN 2014). Both our PSPACE-hardness and the previous NP-hardness use levels of arbitrary dimensions and require either arbitrarily large screens or a game engine that remembers the state of off-screen sprites. We also analyze the comple...

Is Belief Revision Harder Than You Thought?

Suppose one wishes to construct, use, and maintain a knowledge base (KB) of beliefs about the real world, even though the facts about that world are only partially known. In the AI domain, this problem arises when an agent has a base set of beliefs that reflect partial knowledge about the world, and then tries to incorporate new, possibly contradictory knowledge into this set of beliefs. We cho...

Data Integration Is Harder than You Thought

More ties than we thought

We extend the existing enumeration of neck tie knots to include tie knots with a textured front, tied with the narrow end of a tie. These tie knots have gained popularity in recent years, based on reconstructions of a costume detail from The Matrix Reloaded, and are explicitly ruled out in the enumeration by Fink and Mao (2000). We show that the relaxed tie knot description language that compre...

Total Order Planning is More Efficient than we Thought

In this paper, we present VVPLAN, a planner based on a classical state space search algorithm. The language used for domain and problem representation is ADL (Pednault 1989). We have compared VVPLAN to UCPOP (Penberthy and Weld 1992)(Weld 1994), a planner that admits the same representation language. Our experiments prove that such an algorithm is often more efficient than a planner based on a ...