Progress in AI Planning Research and Applications

Planning has made significant progress since its inception in the 1970s, in terms both of the efficiency and sophistication of its algorithms and representations and its potential for application to real problems. In this paper we sketch the foundations of planning as a sub-field of Artificial Intelligence and the history of its development over the past three decades. Then some of the recent achievements within the field are discussed and provided some experimental data demonstrating the progress that has been made in the application of general planners to realistic and complex problems. The paper concludes by identifying some of the open issues that remain as important challenges for future research in planning

Process algebra for performance evaluation

This paper surveys the theoretical developments in the field of stochastic process algebras, process algebras where action occurrences may be subject to a delay that is determined by a random variable. A huge class of resource-sharing systems – like large-scale computers, client–server architectures, networks – can accurately be described using such stochastic specification formalisms. The main emphasis of this paper is the treatment of operational semantics, notions of equivalence, and (sound and complete) axiomatisations of these equivalences for different types of Markovian process algebras, where delays are governed by exponential distributions. Starting from a simple actionless algebra for describing time-homogeneous continuous-time Markov chains, we consider the integration of actions and random delays both as a single entity (like in known Markovian process algebras like TIPP, PEPA and EMPA) and as separate entities (like in the timed process algebras timed CSP and TCCS). In total we consider four related calculi and investigate their relationship to existing Markovian process algebras. We also briefly indicate how one can profit from the separation of time and actions when incorporating more general, non-Markovian distributions

Public Firms in a Dynamic Third Market Model

We set the third market model in a dynamic context to decide whether a country can achieve benefits by subsidizing a public rm's exports. We use calculus of variations with the constraint that the welfare is either maximized or grows at constant rate, reflecting the public concern of the firm. We conclude that a subsidy can be a good strategy for the country in some instances, even though only over a finite period of time. The duration of this period depends on the output strategy of the public firm as well as on exogenous factors.public firms, strategic trade policy, third market model, calculus of variations

An application of the option-pricing model to the valuation of football player in the ‘Serie A League’

Football is perhaps the most popular sport in the world. The market of football players is one of the most popular factors of the sport that makes the fans dream of each team which increases the interest around the sport. In 2013 the player Gareth Bale was sold from Tottenham to Real Madrid for 100 million Euros. Someone argues that the market for football players is inherently irrational precisely because of the sale price of certain players. This paper is based on Tunaru et al. model that is real option based model. The aim of the paper is the financial valuation of a goalkeeper of Serie A League club. The model depends on relationship of player’s and team’s performance and the club’s turnover

Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks

Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems. However, a major obstacle in applying them to safety-critical systems is the great difficulty in providing formal guarantees about their behavior. We present a novel, scalable, and efficient technique for verifying properties of deep neural networks (or providing counter-examples). The technique is based on the simplex method, extended to handle the non-convex Rectified Linear Unit (ReLU) activation function, which is a crucial ingredient in many modern neural networks. The verification procedure tackles neural networks as a whole, without making any simplifying assumptions. We evaluated our technique on a prototype deep neural network implementation of the next-generation airborne collision avoidance system for unmanned aircraft (ACAS Xu). Results show that our technique can successfully prove properties of networks that are an order of magnitude larger than the largest networks verified using existing methods.Comment: This is the extended version of a paper with the same title that appeared at CAV 201