This article gives an introduction to agent-based modeling and simulation (ABMS). After a general discussion about modeling and simulation, we address the basic concept of ABMS, focusing on its generative and bottom-up nature, its advantages as well as its pitfalls. The subsequent part of the article deals with application-oriented aspects, including selected tools and well-known applications. In order to illustrate the benefits of using ABMS, we focus on several aspects of a well-known area related to simulation of complex systems, namely traffic. At the end, a brief look into future challenges is given.

Supply Chain coordination has become a critical success factor for Supply Chain management (SCM) and effectively improving the performance of organizations in various industries. Companies are increasingly located at the intersection of one or more corporate networks which are designated by "Supply Chain". Managing this chain is mainly based on an 'information sharing' and redeployment activities between the various links that comprise it. Several attempts have been made by industrialists and researchers to educate policymakers about the gains to be made by the implementation of cooperative relationships. The approach presented in this paper here is among the works that aim to propose solutions related to information systems distributed Supply Chains to enable the different actors of the chain to improve their performance. We propose in particular solutions that focus on cooperation between actors in the Supply Chain.

The current paper introduces a new parallel computing technique based on ant colony optimization for a dynamic routing problem. In the dynamic traveling salesman problem the distances between cities as travel times are no longer fixed. The new technique uses a parallel model for a problem variant that allows a slight movement of nodes within their Neighborhoods. The algorithm is tested with success on several large data sets.

We present a distributed proximal-gradient method for optimizing the average of convex functions, each of which is the private local objective of an agent in a network with time-varying topology. The local objectives have distinct differentiable components, but they share a common nondifferentiable component, which has a favorable structure suitable for effective computation of the proximal operator. In our method, each agent iteratively updates its estimate of the global minimum by optimizing its local objective function, and exchanging estimates with others via communication in the network. Using Nesterov-type acceleration techniques and multiple communication steps per iteration, we show that this method converges at the rate 1/k (where k is the number of communication rounds between the agents), which is faster than the convergence rate of the existing distributed methods for solving this problem. The superior convergence rate of our method is also verified by numerical experiments.

The word autonomy is often used in the discussion of software-based music-generating systems. Whilst the term conveys a very clear concept — the sense of self-determination of a system — attempts to formalise autonomy are at an early stage, and the term is subject to a range of interpretations when practically applied. We consider how the evaluation of music-generating systems will be enhanced by a clearer understanding of autonomy and its application to music. We discuss existing definitions and approaches to quantifying autonomy and consider, through a series of examples, the information that is required in order to make precise formal judgements about autonomy, and the identification of relevant levels at which the principle of autonomy applies in music. We conclude that automated measures can supplement human evaluation of autonomy, but that (a) automated measures must be supported by sound reasoning about the features and timescales used in the measurement, and (b) they are improved by a having knowledge of the internal working of the system, rather than taking a black box approach. We consider multi-dimensional representations of system behaviour that may capture a richer sense of the notion of autonomy. Finally, we propose an approach to automatically probing music systems as a means of determining an autonomy `portrait'.

Artificial Intelligence (AI) and Personalization are both essential - How do we relate content (the factual knowledge aspects of all games, be they serious or entertainment contained, game mechanics) and context (experiences based. In this research the role of AI and Personalization is and activities) to pedagogical goals towards supporting however focused upon the context of Serious Games (SG) in pedagogically-driven design and development of SGs? particular. A concerted research direction is necessary in this From these two high-level questions we derived a more area so as to establish future benchmarks and metrics for the pragmatic approach to AI and Personalization based on: In effective use of AI and Personalization in serious games design what ways can personalization improve learning and adapt and will benefit relevant research communities in providing best to learner requirements?

In this paper, methods and models for the design of educational interventions and usable systems are presented and synthesized. The purpose is to suplliment the design process with educational considerations and discern design principles for the development of serious STEM games. This synthesis can contribute to the design of the next generation of technologically enhanced learning environments.

Location-based games are an emerging paradigm for training, simulation, entertainment, health and many other domains. In this paper, we consider the role of location-based games as a platform for data collection and analysis of human behavior. We also examine how human teams perform in a disaster scenario when such a scenario is mapped to a game environment conducted as a location-based augmented reality game. We use a pilot experiment to study human behavior between simulated disaster rescue teams and an integrated commander for the purpose of future research into improving exploitation of local tasks versus exploration of assigned objectives by disaster response teams. We show the results of our pilot experiment, analyze the effectiveness of this game as a data collection platform and then investigate how additional experiments may be conducted to formalize this problem further.

Autonomous aesthetic evaluation is the Holy Grail of generative music, and one of the great challenges of computational creativity. Unlike most other computational activities, there is no notion of optimality in evaluating creative output: there are subjective impressions involved, and framing obviously plays a big role. When developing metacreative systems, a purely objective fitness function is not available: the designer is thus faced with how much of their own aesthetic to include. Can a generative system be free of the designer’s bias? This paper presents a system that incorporates an aesthetic selection process that allows for both human-designed and non-human fitness functions.

Beyond the goal of refining system design to the needs and tastes of users, user evaluation of interactive music systems offers a method of examining the nature of musical creativity as understood by its human practitioners. In the case of improvising music systems, user study and evaluation of a system’s ability to improvise may be useful in the ethnomusicological study of musical interaction in contemporary improvised music. A survey of preliminary findings based on the interactions of an improvising system, Maxine, with several improvisers is discussed, with results suggesting methodological reconfigurations of the purpose and goals of evaluating of interactive musical metacreations.