A Game Engine based Networked Infrastructure to Create and Share 3D Abstract Art

Online communities have been proactive in producing collaborative creative content such as music, games and other social interactions. Online collaboration has enabled contributors to peer produce and share masses of creative content. Examples range from information sharing such as Wikipedia to open source software and other specific art projects. Software vendors have recently introduced low cost 2D and 3D content authoring tools allowing user communities to generate and share creative content. Emerging networking programming interfaces available inside modern game engines allow contributors to implement multiplayer or multiuser interaction relatively easily. This paper presents a 3D art creation framework to be used over networked infrastructure in a multiuser environment. Contributors will be able to create 3D sculptures at runtime, share with other users in a common networked working environment and critique each other’s work. Experimental work also involved evaluating procedurally generated meshes versus instantiation of primitive mesh objects. Saving and loading mesh information in an optimum way is also explored

Algorithmic Settlements

Utilizing a typological understanding of the spatial conditions of informal settlements to help suggest how interventions will affect conditions is the goal. Understanding the beneficial aspects of communal space created as well as the nature of adaptability within the settlement provides architects with a better understanding of where and how they might intervene. Simulating the conditions of an informal settlement digitally would help to better understand the individualistic agency involved in their creation. Modeling the inter-relational actions between neighboring entities within the system allows for the system to be understood more complexly than before. Designers would be able to add this tool to their repertoire in order to broaden the scope with which they operate on informal settlements

A Game Engine based Networked Infrastructure to Create and Share 3D Abstract Art

Online communities have been proactive in producingcollaborative creative content such as music, games and othersocial interactions. Online collaboration has enabled contributorsto peer produce and share masses of creative content. Examplesrange from information sharing such as Wikipedia to opensource software and other specific art projects. Software vendorshave recently introduced low cost 2D and 3D content authoringtools allowing user communities to generate and share creativecontent. Emerging networking programming interfaces availableinside modern game engines allow contributors to implementmultiplayer or multiuser interaction relatively easily. This paperpresents a 3D art creation framework to be used over networkedinfrastructure in a multiuser environment. Contributors will beable to create 3D sculptures at runtime, share with other users ina common networked working environment and critique eachother’s work. Experimental work also involved evaluatingprocedurally generated meshes versus instantiation of primitivemesh objects. Saving and loading mesh information in anoptimum way is also explored

What Is Collective Intelligence?

Chapter 1 introduces collective intelligence (CI) as an academic concept. At a basic level, CI extends the conception of intelligence from an individual to a group level. Pierre Lévy formulated the modern version in 1994, when he described the invention of the Internet as a new universally distributed intelligence. Today, CI covers many different areas, but most definitions are vague and inconsistent across academic disciplines. Studies address collective problem solving in both small and large groups. At a micro level, researchers have identified a general group intelligence factor that is relevant for performance in small groups. At a macro level, studies of large groups have focused on different types of self-organization, including both stigmergy and swarm coordination. In addition, diversity examines CI as a core feature, both from the perspective of the “many wrongs principle” and the “many eyes principle.” Furthermore, the chapter provides a description of the book’s theoretical approach, building on Vygotsky and the inclusion of both biological and cultural-historical perspectives. The section on the methodological approach explains the data collection process, and the use of top solver perceptions of their participation in online innovation contests.publishedVersio

Socio-cognitively inspired ant colony optimization

Recently we proposed an application of ant colony optimization (ACO) to simulate socio-cognitive features of a population, incorporating perspective-taking ability to generate differently acting ant colonies. Although our main goal was simulation, we took advantage of the fact that the quality of the constructed system was evaluated based on selected traveling salesman problem instances, and the resulting computing system became a metaheuristic, which turned out to be a promising method for solving discrete problems. In this paper, we extend the initial sets of populations driven by different perspective-taking inspirations, seeking both optimal configuration for solving a number of TSP benchmarks, at the same time constituting a tool for analyzing socio-cognitive features of the individuals involved. The proposed algorithms are compared against classic ACO, and are found to prevail in most of the benchmark functions tested

Active Inferants: An Active Inference Framework for Ant Colony Behavior

In this paper, we introduce an active inference model of ant colony foraging behavior, and implement the model in a series of in silico experiments. Active inference is a multiscale approach to behavioral modeling that is being applied across settings in theoretical biology and ethology. The ant colony is a classic case system in the function of distributed systems in terms of stigmergic decision-making and information sharing. Here we specify and simulate a Markov decision process (MDP) model for ant colony foraging. We investigate a well-known paradigm from laboratory ant colony behavioral experiments, the alternating T-maze paradigm, to illustrate the ability of the model to recover basic colony phenomena such as trail formation after food location discovery. We conclude by outlining how the active inference ant colony foraging behavioral model can be extended and situated within a nested multiscale framework and systems approaches to biology more generally