100,704 research outputs found
Revisiting Robustness in Priced Timed Games
Priced timed games are optimal-cost reachability games played between two
players---the controller and the environment---by moving a token along the
edges of infinite graphs of configurations of priced timed automata. The goal
of the controller is to reach a given set of target locations as cheaply as
possible, while the goal of the environment is the opposite. Priced timed games
are known to be undecidable for timed automata with or more clocks, while
they are known to be decidable for automata with clock.
In an attempt to recover decidability for priced timed games Bouyer, Markey,
and Sankur studied robust priced timed games where the environment has the
power to slightly perturb delays proposed by the controller. Unfortunately,
however, they showed that the natural problem of deciding the existence of
optimal limit-strategy---optimal strategy of the controller where the
perturbations tend to vanish in the limit---is undecidable with or more
clocks. In this paper we revisit this problem and improve our understanding of
the decidability of these games. We show that the limit-strategy problem is
already undecidable for a subclass of robust priced timed games with or
more clocks. On a positive side, we show the decidability of the existence of
almost optimal strategies for the same subclass of one-clock robust priced
timed games by adapting a classical construction by Bouyer at al. for one-clock
priced timed games
An Evolutionary Learning Approach for Adaptive Negotiation Agents
Developing effective and efficient negotiation mechanisms for real-world applications such as e-Business is challenging since negotiations in such a context are characterised by combinatorially complex negotiation spaces, tough deadlines, very limited information about the opponents, and volatile negotiator preferences. Accordingly, practical negotiation systems should be empowered by effective learning mechanisms to acquire dynamic domain knowledge from the possibly changing negotiation contexts. This paper illustrates our adaptive negotiation agents which are underpinned by robust evolutionary learning mechanisms to deal with complex and dynamic negotiation contexts. Our experimental results show that GA-based adaptive negotiation agents outperform a theoretically optimal negotiation mechanism which guarantees Pareto optimal. Our research work opens the door to the development of practical negotiation systems for real-world applications
Design and Analysis of an Estimation of Distribution Approximation Algorithm for Single Machine Scheduling in Uncertain Environments
In the current work we introduce a novel estimation of distribution algorithm
to tackle a hard combinatorial optimization problem, namely the single-machine
scheduling problem, with uncertain delivery times. The majority of the existing
research coping with optimization problems in uncertain environment aims at
finding a single sufficiently robust solution so that random noise and
unpredictable circumstances would have the least possible detrimental effect on
the quality of the solution. The measures of robustness are usually based on
various kinds of empirically designed averaging techniques. In contrast to the
previous work, our algorithm aims at finding a collection of robust schedules
that allow for a more informative decision making. The notion of robustness is
measured quantitatively in terms of the classical mathematical notion of a norm
on a vector space. We provide a theoretical insight into the relationship
between the properties of the probability distribution over the uncertain
delivery times and the robustness quality of the schedules produced by the
algorithm after a polynomial runtime in terms of approximation ratios
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