12,595 research outputs found
Learning in Repeated Games: Human Versus Machine
While Artificial Intelligence has successfully outperformed humans in complex
combinatorial games (such as chess and checkers), humans have retained their
supremacy in social interactions that require intuition and adaptation, such as
cooperation and coordination games. Despite significant advances in learning
algorithms, most algorithms adapt at times scales which are not relevant for
interactions with humans, and therefore the advances in AI on this front have
remained of a more theoretical nature. This has also hindered the experimental
evaluation of how these algorithms perform against humans, as the length of
experiments needed to evaluate them is beyond what humans are reasonably
expected to endure (max 100 repetitions). This scenario is rapidly changing, as
recent algorithms are able to converge to their functional regimes in shorter
time-scales. Additionally, this shift opens up possibilities for experimental
investigation: where do humans stand compared with these new algorithms? We
evaluate humans experimentally against a representative element of these
fast-converging algorithms. Our results indicate that the performance of at
least one of these algorithms is comparable to, and even exceeds, the
performance of people
The Hanabi Challenge: A New Frontier for AI Research
From the early days of computing, games have been important testbeds for
studying how well machines can do sophisticated decision making. In recent
years, machine learning has made dramatic advances with artificial agents
reaching superhuman performance in challenge domains like Go, Atari, and some
variants of poker. As with their predecessors of chess, checkers, and
backgammon, these game domains have driven research by providing sophisticated
yet well-defined challenges for artificial intelligence practitioners. We
continue this tradition by proposing the game of Hanabi as a new challenge
domain with novel problems that arise from its combination of purely
cooperative gameplay with two to five players and imperfect information. In
particular, we argue that Hanabi elevates reasoning about the beliefs and
intentions of other agents to the foreground. We believe developing novel
techniques for such theory of mind reasoning will not only be crucial for
success in Hanabi, but also in broader collaborative efforts, especially those
with human partners. To facilitate future research, we introduce the
open-source Hanabi Learning Environment, propose an experimental framework for
the research community to evaluate algorithmic advances, and assess the
performance of current state-of-the-art techniques.Comment: 32 pages, 5 figures, In Press (Artificial Intelligence
A semantical approach to equilibria and rationality
Game theoretic equilibria are mathematical expressions of rationality.
Rational agents are used to model not only humans and their software
representatives, but also organisms, populations, species and genes,
interacting with each other and with the environment. Rational behaviors are
achieved not only through conscious reasoning, but also through spontaneous
stabilization at equilibrium points.
Formal theories of rationality are usually guided by informal intuitions,
which are acquired by observing some concrete economic, biological, or network
processes. Treating such processes as instances of computation, we reconstruct
and refine some basic notions of equilibrium and rationality from the some
basic structures of computation.
It is, of course, well known that equilibria arise as fixed points; the point
is that semantics of computation of fixed points seems to be providing novel
methods, algebraic and coalgebraic, for reasoning about them.Comment: 18 pages; Proceedings of CALCO 200
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