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This study aims to propose a new educational tool for interacting with artificial life (ALife). The proposed system consists of an ALife behavior simulation and a tactile aerial display with a combination of aerial imaging by retro-reflection and a force-feedback device. The content is mid-air interaction with a simulated school of fish-like ALife. The behavior is based on a modified BOIDs algorithm with a predator-prey relationship between different species. A user can enjoy interaction with the fish through aerial imaging and haptic feeling. The user can feel the activity of ALife as a force field using haptics.

Boids is an excellent example of emergent behaviour. Coding some simple rules creates complex behaviour. The assignment consolidates students' learning of C++, OpenGL, GLM, and vector arithmetic. Students also learn about the careful balances that must be made to ensure that a simulation behaves in a realistic way.

This paper presents an evolutionary computing based approach to automatically synthesise swarm behavioural rules from their atomic components, thus making a step forward in trying to mitigate human bias from the rule generation process, and leverage the full potential of swarm systems in the real world by modelling more complex behaviours. We identify four components that make-up the structure of a rule: control structures, parameters, logical/relational connectives and preliminary actions, which form the rule space for the proposed approach. A boids simulation system is employed to evaluate the approach with grammatical evolution and genetic programming techniques using the rule space determined. While statistical analysis of the results demonstrates that both methods successfully evolve desired complex behaviours from their atomic components, the grammatical evolution model shows more potential in generating complex behaviours in a modularised approach. Furthermore, an analysis of the structure of the evolved rules implies that the genetic programming approach only derives non-reusable rules composed of a group of actions that is combined to result in emergent behaviour. In contrast, the grammatical evolution approach synthesises sound and stable behavioural rules which can be extracted and reused, hence making it applicable in complex application domains where manual design is infeasible.

This project proposes an enhancement of MitsuDomoe at ReVolution 2017. The new feature is tactile aerial imaging by combining retro-reflective optics and haptic interface. New application for the improved system presents novel interaction with artificial life. The application is mid-air interaction with virtual schools of fish-like artificial life. It looks like bacteria in terms of size but the behavior seems fish. The fish has rule-based pseudo intelligence based on Reynolds' BOIDs algorithm. User can enjoy interaction with the primitive artificial life through aerial imaging and haptic feeling; the school of fish will spread when a user stirs the school of fish. And the school of fish separates and/or gathers when the user gives feed.It makes fully use of the synergistic advantage of the functions to provide realistic feeling to the interaction. The force feedback function allows the user to feel of stirring and/or touching artificial lives in viscous liquid or gel material filled in the petri dish. And aerial imaging function allows the users to observe pseudo volumetric image of the petri dish, which is filled with liquid and virtual creatures moving around in it.The first prototype system of MitsuDomoe, an interactive ecosystem simulator of virtual creatures in a petri dish, comprises three species of primitive artificial creatures. MitsuDomoe simulates the predation chain of the virtual creatures in the petri dish. Users can interact with this ecosystem via the physical MR interface, which consists of a LCD module with petri dishes and a pipette-like device. The physical MR interface can provide users with the sense and enjoyment of performing experiments in a laboratory.

As the development of technology, the demand of operating UAVs (Unmanned Aerial Vehicle) in group grows larger. In the operation of group UAVs, it is important that who the leaders should be and who the followers will be. We use multi agent technique to this problem. This study proposes the mechanism of leader selection based on influence of an agent to its neighbors' movement. The influence value can be calculated by the forces from neighborhood agents. In the proposed method, the leaders are selected dynamically and adaptively. Simple computer simulation shows the effectiveness of the method.

Since the Boids model was proposed by Reynolds(1987), grouping behavior of animals and agents have been extensively studied and applied. Recent improvement in movie analysis enabled the discovery of scale-free correlation and phase transition in real flocks. We execute the comparison of two kinds of distances that define the behavior of the individuals in groups.

Emergence is a distinguishing feature in systems, especially when complexity grows with the number of components, interactions, and connectivity. There is immense interest in emergence, and a plethora of definitions from philosophy to sciences. Despite this, there is a lack of consensus on the definition of emergence and this hinders the development of a formal approach to understand and predict emergent behavior in multi-agent systems. This paper proposes a grammar-based set-theoretic approach to formalize and verify the existence and extent of emergence without prior knowledge or definition of emergent properties. Our approach is based on weak (basic) emergence that is both generated and autonomous from the underlying agents. In contrast with current work, our approach has two main advantages. By focusing only on system interactions of interest and feasible combinations of individual agent behavior, state-space explosion is reduced. In formalizing emergence, our extended grammar is designed to model agents of diverse types, mobile agents, and open systems. Theoretical and experimental studies using the boids model demonstrate the complexity of our formal approach.

The emergent discussion about embodiment offers new perspectives in Human-Computer-Interaction and needs further fundamental research in particular for its usefulness in the field of education. With our approach we want to use the concept to foster children's motivation to gain insight into the secrets of "intelligent" behavior of computer-generated artifacts. In this paper, our leading question is: Does bodily engagement support children in learning abstract models implemented through digital technologies better than through traditional software applications? We developed an installation Der Schwarm, which aims at motivating children to learn by whole body interaction as well as a learning software application Boids Regeln. We conducted and evaluated the study during a workshop with children aged 9-10 years. Finally we discuss the method and the results of this comparative, qualitative study.

Crowds and other flock-like group motion are often modeled as interacting particle systems. These multi-agent simulations are computationally expensive because each agent must consider all of the others, if only to identify its neighbors. For large crowds, simple implementations are too slow since computation grows as the square of agent population. Faster approaches often rely on spatial hashing where a partitioning of space is used to accelerate crowd simulation. This same partitioning can form the basis of a scalable multi-processor approach to large, fast crowd simulations, as in [Quinn et al. 2003]. This paper describes an implementation of that approach for PLAYSTATION®3 which supports simulation and display of simple crowds of up to 15,000 individuals at 60 frames per second.