34,201 research outputs found
IST Austria Technical Report
Simulation is an attractive alternative for language inclusion for automata as it is an under-approximation of language inclusion, but usually has much lower complexity. For non-deterministic automata, while language inclusion is PSPACE-complete, simulation can be computed in polynomial time. Simulation has also been extended in two orthogonal directions, namely, (1) fair simulation, for simulation over specified set of infinite runs; and (2) quantitative simulation, for simulation between weighted automata. Again, while fair trace inclusion is PSPACE-complete, fair simulation can be computed in polynomial time. For weighted automata, the (quantitative) language inclusion problem is undecidable for mean-payoff automata and the decidability is open for discounted-sum automata, whereas the (quantitative) simulation reduce to mean-payoff games and discounted-sum games, which admit pseudo-polynomial time algorithms.
In this work, we study (quantitative) simulation for weighted automata with Büchi acceptance conditions, i.e., we generalize fair simulation from non-weighted automata to weighted automata. We show that imposing Büchi acceptance conditions on weighted automata changes many fundamental properties of the simulation games. For example, whereas for mean-payoff and discounted-sum games, the players do not need memory to play optimally; we show in contrast that for simulation games with Büchi acceptance conditions, (i) for mean-payoff objectives, optimal strategies for both players require infinite memory in general, and (ii) for discounted-sum objectives, optimal strategies need not exist for both players. While the simulation games with Büchi acceptance conditions are more complicated (e.g., due to infinite-memory requirements for mean-payoff objectives) as compared to their counterpart without Büchi acceptance conditions, we still present pseudo-polynomial time algorithms to solve simulation games with Büchi acceptance conditions for both weighted mean-payoff and weighted discounted-sum automata
Fair Simulation for Nondeterministic and Probabilistic Buechi Automata: a Coalgebraic Perspective
Notions of simulation, among other uses, provide a computationally tractable
and sound (but not necessarily complete) proof method for language inclusion.
They have been comprehensively studied by Lynch and Vaandrager for
nondeterministic and timed systems; for B\"{u}chi automata the notion of fair
simulation has been introduced by Henzinger, Kupferman and Rajamani. We
contribute to a generalization of fair simulation in two different directions:
one for nondeterministic tree automata previously studied by Bomhard; and the
other for probabilistic word automata with finite state spaces, both under the
B\"{u}chi acceptance condition. The former nondeterministic definition is
formulated in terms of systems of fixed-point equations, hence is readily
translated to parity games and is then amenable to Jurdzi\'{n}ski's algorithm;
the latter probabilistic definition bears a strong ranking-function flavor.
These two different-looking definitions are derived from one source, namely our
coalgebraic modeling of B\"{u}chi automata. Based on these coalgebraic
observations, we also prove their soundness: a simulation indeed witnesses
language inclusion
Statistics of the Kolkata Paise Restaurant Problem
We study the dynamics of a few stochastic learning strategies for the
'Kolkata Paise Restaurant' problem, where N agents choose among N equally
priced but differently ranked restaurants every evening such that each agent
tries get to dinner in the best restaurant (each serving only one customer and
the rest arriving there going without dinner that evening). We consider the
learning strategies to be similar for all the agents and assume that each
follow the same probabilistic or stochastic strategy dependent on the
information of the past successes in the game. We show that some 'naive'
strategies lead to much better utilization of the services than some relatively
'smarter' strategies. We also show that the service utilization fraction as
high as 0.80 can result for a stochastic strategy, where each agent sticks to
his past choice (independent of success achieved or not; with probability
decreasing inversely in the past crowd size). The numerical results for
utilization fraction of the services in some limiting cases are analytically
examined.Comment: 10 pages, 3 figs; accepted in New J Phy
Company-university collaboration in applying gamification to learning about insurance
Incorporating gamification into training–learning at universities is hampered by a shortage of quality, adapted educational video games. Large companies are leading in the creation of educational video games for their internal training or to enhance their public image and universities can benefit from collaborating. The aim of this research is to evaluate, both objectively and subjectively, the potential of the simulation game BugaMAP (developed by the MAPFRE Foundation) for university teaching about insurance. To this end, we have assessed both the game itself and the experience of using the game as perceived by 142 economics students from various degree plans and courses at the University of Seville during the 2017–2018 academic year. As a methodology, a checklist of gamification components is used for the objective evaluation, and an opinion questionnaire on the game experience is used for the subjective evaluation. Among the results several findings stand out. One is the high satisfaction of the students with the knowledge acquired using fun and social interaction. Another is that the role of the university professors and the company monitors turns out to be very active and necessary during the game-learning sessions. Finally, in addition to the benefits to the university of occasionally available quality games to accelerate student skills training, the company–university collaboration serves as a trial and refinement of innovative tools for game-based learning
Industrial Symbiotic Relations as Cooperative Games
In this paper, we introduce a game-theoretical formulation for a specific
form of collaborative industrial relations called "Industrial Symbiotic
Relation (ISR) games" and provide a formal framework to model, verify, and
support collaboration decisions in this new class of two-person operational
games. ISR games are formalized as cooperative cost-allocation games with the
aim to allocate the total ISR-related operational cost to involved industrial
firms in a fair and stable manner by taking into account their contribution to
the total traditional ISR-related cost. We tailor two types of allocation
mechanisms using which firms can implement cost allocations that result in a
collaboration that satisfies the fairness and stability properties. Moreover,
while industries receive a particular ISR proposal, our introduced methodology
is applicable as a managerial decision support to systematically verify the
quality of the ISR in question. This is achievable by analyzing if the
implemented allocation mechanism is a stable/fair allocation.Comment: Presented at the 7th International Conference on Industrial
Engineering and Systems Management (IESM-2017), October 11--13, 2017,
Saarbr\"ucken, German
Discrete--time ratchets, the Fokker--Planck equation and Parrondo's paradox
Parrondo's games manifest the apparent paradox where losing strategies can be
combined to win and have generated significant multidisciplinary interest in
the literature. Here we review two recent approaches, based on the
Fokker-Planck equation, that rigorously establish the connection between
Parrondo's games and a physical model known as the flashing Brownian ratchet.
This gives rise to a new set of Parrondo's games, of which the original games
are a special case. For the first time, we perform a complete analysis of the
new games via a discrete-time Markov chain (DTMC) analysis, producing winning
rate equations and an exploration of the parameter space where the paradoxical
behaviour occurs.Comment: 17 pages, 5 figure
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