Igor Tomičić

A roadmap for scalable agent organizations in the Internet of Everything

Establishes a new direction in software engineering for the Internet of Things.Argues that we will soon have to manage systems where millions of agents interact.Proposes organization theory as the necessary foundation ontology.Provides examples of possible application areasGives a roadmap for future developments. Display Omitted Computing is increasingly ubiquitous, with everyday items including smartphones, cars, clothes and household appliances gaining increasingly sophisticated computing and communication capacities. With the development of the Internet of Things, it is just a matter of time before devices have to collaborate and compete with each other, in order to provide better services to mankind.These embedded software systems are increasingly autonomous and connected, and can thus be modeled as multiagent systems (MAS). Only 30 years ago it was science fiction that over a billion people will exchange billions of e-mails on a daily basis. Today a scenario of millions of collaborating agents sometimes embedded in gadgets and appliances, sometimes in form of networked and big data services, may also sound futuristic. However given the current rate of development in electronics, we will soon have to manage large scale MAS where millions of agents exist, collaborate and compete. Organization theory provides the necessary methodology to approach complex systems in order to design, implement and strategically manage them towards success.In this paper a state-of-the-art on organizational design techniques for large-scale MAS will be presented, missing advancements will be identified and a roadmap for future developments and application scenarios will be provided.

Multi-agent modeling methods for massivley Multi-Player On-Line Role-Playing Games

Massively Multi-Player On-Line Role-Playing Games (MMORPG) give us the opportunity to study two important aspects of computing: (1) large-scale virtual social interaction of people (players) and (2) the design, development and coordination of large-scale distributed artificial intelligence (AI). A common denominator for both aspects are the methods used to study them: social interaction can be described and simulated using agent-based models (ABM) whilst distributed AI is commonly modeled in terms of multi-agent systems (MAS). The important question to ask in both perspectives is how do agents organize in order to perform their tasks and reach their objectives? In the following paper we will present and overview of agent-based approaches to modeling MMORPGs agents including human players, artificial players (bots) and non-playing characters (NPCs). We will put a special accent on useful agent behaviors, coordination and consensus mechanisms as well as organization and social network structure of agent societies.

Agent-based framework for modeling and simulation of resources in self-sustainable human settlements: a case study on water management in an eco-village community in Croatia

ABSTRACT A newly developed agent-based framework for modeling, simulation, and evaluation of resource management in self-sustainable human settlements is presented, along with the ability of the framework to prolong the self-sustainability of the observed human settlement system in the simulation environment. In this study the focus is on the analysis of a conducted water management simulation based on observations of an existing eco-village in Croatia, in the context of producing, storing, and consuming water as a resource. The conducted research shows that the developed framework was able to prolong the self-sustainability of localized water production, storage, and consumption dynamics when managing the water allocation with the use of self-sustainability mechanisms, in comparison to water management without using such mechanisms, under the same initial conditions. The work is placed within the context of sustainable development, Internet of Things, as well as Environmental Internet of Things areas of research, analyzing the ambiguous relationship between the terms ‘sustainable development’ and ‘self-sustainability’.

Privacy improvement model for biometric person recognition in ambient intelligence using perceptual hashing

In the past two decades ambient intelligence (AmI) has been a focus of research in different fields and from different points of view. It can be defined as an electronic environment consisting of devices capable of recognising people presence and responding in a certain way. The security and privacy in these kind of environments is still a challenge. With employing biometrics for person recognition in ambient intelligence, the devices could distinguish between different people in a non-intrusive way. With this, the privacy issue occurring in ambient intelligence is even more pronounced when combined with biometric recognition. This paper shows a privacy improvement model for biometric person recognition in ambient intelligence using perceptual hashing.

Modeling Smart Self-sustainable Cities as Large- Scale Agent Organizations in the IoT Environment

This chapter provides an overview of modeling techniques for large-scale systems in the Internet of Things (IoT) environment with a special accent on smart self-sustainable cities. The authors present a framework for modeling Large-Scale Multi-Agent Systems (LSMASs) including a graphical modeling language, and a tool, that aim to facilitate development of such systems in a recursive fashion. Smart self-sustainable cities in this chapter are modeled using this language that forms the basis for the Smart Self-Sustainable Human Settlements (SSSHS) framework developed by the authors. The SSSHS framework consists of several sustainability mechanisms which attempt to facilitate the self-sustainability of a human settlement by managing resources such as water, electricity, and heating, based on the current needs, production, and storage using a detailed agent-based methodology. By integrating these two frameworks (LSMAS and SSSHS), the authors show a recursive and layered approach that is able to model large-scale resource management systems in a hierarchical manner by using IoT technologies.

Agents as Bots – An Initial Attempt Towards Model-Driven MMORPG Gameplay

Massively multi-player on-line role-playing games (MMORPGs) present a large-scale, digital environment that fosters organizational behaviour of players in which multi-agent systems (MASs) can be used for various purposes including but not limited to automated testing, bot detection or analysis of social player behaviour and human – artificial agent interaction. A work-in-progress model-driven MAS development environment for such games is presented. An open-source MMORPG called The Mana World (TMW) is used as an example scenario on which the various components of the system are tested.

Automated MMORPG Testing – An Agent-Based Approach

A work-in-progress agent-based framework for automated testing of an open-source massively multi-player on-line role playing game (MMORPG) called The Mana World is presented. The implemented system, in its current state, allows for model-driven development of tests using a graphical user interface (GUI), implementation of automated artificial players (bots) and their use in testing the quests (player tasks) of the game. The system is implemented using Python, SPADE, SWI Prolog and AToM\(^3\).

Towards an agent-based automated testing environment for massively multi-player role playing games

Automated testing in massively multi-player on-line role playing games (MMORPG) is a challenging task due to the complexity of such games and their large numbers of mutually distributed but interacting components. Large-scale multi-agent systems (LSMAS) provide us with a suitable formalism to address such complex problems. Herein a first step towards an automated game testing environment, built for the open source Mana World MMORPG, will be presented that allows the implementation of software agents and agent organizations and provide the developers with valuable game-play and testing data.