23,464 research outputs found
Network algebra for synchronous dataflow
We develop an algebraic theory of synchronous dataflow networks. First, a
basic algebraic theory of networks, called BNA (Basic Network Algebra), is
introduced. This theory captures the basic algebraic properties of networks.
For synchronous dataflow networks, it is subsequently extended with additional
constants for the branching connections that occur between the cells of
synchronous dataflow networks and axioms for these additional constants. We
also give two models of the resulting theory, the one based on stream
transformers and the other based on processes as considered in process algebra.Comment: 24 page
Isotactics as a foundation for alignment and abstraction of behavioral models
There are many use cases in business process management that require the comparison of behavioral models. For instance, verifying equivalence is the basis for assessing whether a technical workflow correctly implements a business process, or whether a process realization conforms to a reference process. This paper proposes an equivalence relation for models that describe behaviors based on the concurrency semantics of net theory and for which an alignment relation has been defined. This equivalence, called isotactics, preserves the level of concurrency of aligned operations. Furthermore, we elaborate on the conditions under which an alignment relation can be classified as an abstraction. Finally, we show that alignment relations induced by structural refinements of behavioral models are indeed behavioral abstractions
Theoretical and Practical Advances on Smoothing for Extensive-Form Games
Sparse iterative methods, in particular first-order methods, are known to be
among the most effective in solving large-scale two-player zero-sum
extensive-form games. The convergence rates of these methods depend heavily on
the properties of the distance-generating function that they are based on. We
investigate the acceleration of first-order methods for solving extensive-form
games through better design of the dilated entropy function---a class of
distance-generating functions related to the domains associated with the
extensive-form games. By introducing a new weighting scheme for the dilated
entropy function, we develop the first distance-generating function for the
strategy spaces of sequential games that has no dependence on the branching
factor of the player. This result improves the convergence rate of several
first-order methods by a factor of , where is the branching
factor of the player, and is the depth of the game tree.
Thus far, counterfactual regret minimization methods have been faster in
practice, and more popular, than first-order methods despite their
theoretically inferior convergence rates. Using our new weighting scheme and
practical tuning we show that, for the first time, the excessive gap technique
can be made faster than the fastest counterfactual regret minimization
algorithm, CFR+, in practice
Incompleteness of States w.r.t. Traces in Model Checking
Cousot and Cousot introduced and studied a general past/future-time
specification language, called mu*-calculus, featuring a natural time-symmetric
trace-based semantics. The standard state-based semantics of the mu*-calculus
is an abstract interpretation of its trace-based semantics, which turns out to
be incomplete (i.e., trace-incomplete), even for finite systems. As a
consequence, standard state-based model checking of the mu*-calculus is
incomplete w.r.t. trace-based model checking. This paper shows that any
refinement or abstraction of the domain of sets of states induces a
corresponding semantics which is still trace-incomplete for any propositional
fragment of the mu*-calculus. This derives from a number of results, one for
each incomplete logical/temporal connective of the mu*-calculus, that
characterize the structure of models, i.e. transition systems, whose
corresponding state-based semantics of the mu*-calculus is trace-complete
Expressiveness and Completeness in Abstraction
We study two notions of expressiveness, which have appeared in abstraction
theory for model checking, and find them incomparable in general. In
particular, we show that according to the most widely used notion, the class of
Kripke Modal Transition Systems is strictly less expressive than the class of
Generalised Kripke Modal Transition Systems (a generalised variant of Kripke
Modal Transition Systems equipped with hypertransitions). Furthermore, we
investigate the ability of an abstraction framework to prove a formula with a
finite abstract model, a property known as completeness. We address the issue
of completeness from a general perspective: the way it depends on certain
abstraction parameters, as well as its relationship with expressiveness.Comment: In Proceedings EXPRESS/SOS 2012, arXiv:1208.244
Improving games AI performance using grouped hierarchical level of detail
Computer games are increasingly making use of large environments; however, these are often only sparsely populated with autonomous agents. This is, in part, due to the computational cost of implementing behaviour functions for large numbers of agents.
In this paper we present an optimisation based on level of detail which reduces the overhead of modelling group behaviours, and facilitates the population of an expansive game world.
We consider an environment which is inhabited by many distinct groups of agents. Each group itself comprises individual agents, which are organised using a hierarchical tree structure. Expanding and collapsing nodes within each tree allows the efficient dynamic abstraction of individuals, depending on their proximity to the player. Each branching level represents a different level of detail, and the system is designed to trade off computational performance against behavioural fidelity in a way which is both efficient and seamless to the player.
We have developed an implementation of this technique, and used it to evaluate the associated performance benefits. Our experiments indicate a significant potential reduction in processing time, with the update for the entire AI system taking less than 1% of the time required for the same number of agents without optimisation
Probabilistic Guarantees for Safe Deep Reinforcement Learning
Deep reinforcement learning has been successfully applied to many control
tasks, but the application of such agents in safety-critical scenarios has been
limited due to safety concerns. Rigorous testing of these controllers is
challenging, particularly when they operate in probabilistic environments due
to, for example, hardware faults or noisy sensors. We propose MOSAIC, an
algorithm for measuring the safety of deep reinforcement learning agents in
stochastic settings. Our approach is based on the iterative construction of a
formal abstraction of a controller's execution in an environment, and leverages
probabilistic model checking of Markov decision processes to produce
probabilistic guarantees on safe behaviour over a finite time horizon. It
produces bounds on the probability of safe operation of the controller for
different initial configurations and identifies regions where correct behaviour
can be guaranteed. We implement and evaluate our approach on agents trained for
several benchmark control problems
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