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We present a nondeterministic model of computation based on reversing edge directions in weighted directed graphs with minimum inflow constraints on vertices. Deciding whether this simple graph model can be manipulated in order to reverse the direction of a particular edge is shown to be PSPACE-complete by a reduction from Quantified Boolean Formulas. We(More)
There is a fundamental connection between the notions of game and of computation. At its most basic level, this is implied by any game complexity result, but the connection is deeper than this. One example is the concept of alternating nondeterminism, which is intimately connected with two-player games. In the first half of this thesis, I develop the idea(More)
We present a nondeterministic model of computation based on reversing edge directions in weighted directed graphs with minimum inflow constraints on vertices. Deciding whether this simple graph model can be manipulated in order to reverse the direction of a particular edge is shown to be PSPACE-complete by a reduction from Quantified Boolean Formulas. We(More)
We introduce a simple game family, called Constraint Logic, where players reverse edges in a directed graph while satisfying vertex inflow constraints. This game family can be interpreted in many different game-theoretic settings , ranging from zero-player automata to a more economic setting of team multiplayer games with hidden information. Each setting(More)
Description: The ability to understand and predict behavior in strategic situations, in which an individual's success in making choices depends on the choices of others, has been the domain of game theory since the 1950s. Developing the theories at the heart of game theory has resulted in 8 Nobel Prizes and insights that researchers in many fields continue(More)
Most Artificial Intelligence (AI) work can be characterized as either " high-level " (e.g., logical, symbolic) or " low-level " (e.g., connectionist, behavior-based robotics). Each approach suffers from particular drawbacks. High-level AI uses abstractions that often have no relation to the way real, biological brains work. Low-level AI, on the other hand,(More)