On the advantages of non-cooperative behavior in agent populations

We investigate the amount of cooperation between agents in a population during reward collection that is required to minimize the overall collection time. In our computer simulation agents have the option to broadcast the position of a reward to neighboring agents with a normally distributed certainty. We modify the standard deviation of this certainty to investigate its optimum setting for a varying number of agents and rewards. Results reveal that an optimum exists and that (a) the collection time and the number of agents and (b) the collection time and the number of rewards, follow a power law relationship under optimum conditions. We suggest that the standard deviation can be self-tuned via a feedback loop and list some examples from nature were we believe this self-tuning to take place

Autopoiesis of the artificial: from systems to cognition

In the seminal work on autopoiesis by Varela, Maturana, and Uribe, they start by addressing the confusion between processes that are history dependent and processes that are history independent in the biological world. The former is particularly linked to evolution and ontogenesis, while the latter pertains to the organizational features of biological individuals. Varela, Maturana, and Uribe reject this framework and propose their original theory of autopoietic organization, which emphasizes the strong complementarity of temporal and non-temporal phenomena. They argue that the dichotomy between structure and organization lies at the core of the unity of living systems. By opposing history-dependent and history-independent processes, methodological challenges arise in explaining phenomena related to living systems and cognition. Consequently, Maturana and Varela reject this approach in defining autopoietic organization. I argue, however, that this relationship presents an issue that can be found in recent developments of the science of artificial intelligence (AI) in different ways, giving rise to related concerns. While highly capable AI systems exist that can perform cognitive tasks, their internal workings and the specific contributions of their components to the overall system behavior, understood as a unified whole, remain largely uninterpretable. This article explores the connection between biological systems, cognition, and recent developments in AI systems that could potentially be linked to autopoiesis and related concepts such as autonomy and organization. The aim is to assess the advantages and disadvantages of employing autopoiesis in the synthetic (artificial) explanation for biological cognitive systems and to determine if and how the notion of autopoiesis can still be fruitful in this perspective

Asphalted Road Temperature Variations Due to Wind Turbine Cast Shadows

The contribution of this paper is a technique that in certain circumstances allows one to avoid the removal of dynamic shadows in the visible spectrum making use of images in the infrared spectrum. This technique emerged from a real problem concerning the autonomous navigation of a vehicle in a wind farm. In this environment, the dynamic shadows cast by the wind turbines' blades make it necessary to include a shadows removal stage in the preprocessing of the visible spectrum images in order to avoid the shadows being misclassified as obstacles. In the thermal images, dynamic shadows completely disappear, something that does not always occur in the visible spectrum, even when the preprocessing is executed. Thus, a fusion on thermal and visible bands is performed

Nonparametric regression for multiple heterogeneous networks

We study nonparametric methods for the setting where multiple distinct networks are observed on the same set of nodes. Such samples may arise in the form of replicated networks drawn from a common distribution, or in the form of heterogeneous networks, with the network generating process varying from one network to another, e.g. dynamic and cross-sectional networks. Nonparametric methods for undirected networks have focused on estimation of the graphon model. While the graphon model accounts for nodal heterogeneity, it does not account for network heterogeneity, a feature specific to applications where multiple networks are observed. To address this setting of multiple networks, we propose a multi-graphon model which allows node-level as well as network-level heterogeneity. We show how information from multiple networks can be leveraged to enable estimation of the multi-graphon via standard nonparametric regression techniques, e.g. kernel regression, orthogonal series estimation. We study theoretical properties of the proposed estimator establishing recovery of the latent nodal positions up to negligible error, and convergence of the multi-graphon estimator to the normal distribution. Finite sample performance are investigated in a simulation study and application to two real-world networks--a dynamic contact network of ants and a collection of structural brain networks from different subjects--illustrate the utility of our approach

Disembodied characters

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, September 1999."August 1999."Includes bibliographical references (p. 72-73).A colony of social insects as a whole can be regarded as an organism that reproduces, maintains its internal structure, and survives in a hostile an unpredictable environment. Such superorganism - an entity that consists of smaller component organisms - is able to perform remarkable feats, decentralized information processing among them. For instance, a swarm of bees is able to choose the best possible nesting cavity even though only a few of the individuals have any knowledge of the available sites, and no single bee has a full knowledge of the situation. This decentralized decision making is remarkably similar to that performed by hypothetical functional agents, frequently featured in decentralist theories of the human mind. In this thesis I argue that comparing a superorganism to the mind is useful. In particular, this comparison opens up an enchanting opportunity for the creation of expressive synthetic characters that may become important incremental stepping stones on the way to complex artificial intelligence. In order to explore the space between metaphors - the human mind as a collection of interconnected mindless agents, and the superorganism as a unitary whole that exhibits functional characteristics beyond those of its component parts - I present the design and implementation of the Mask of the Hive, a character that is based on a model of a bee colony. My emphasis lies on graphic design and information visualization in order to develop a set of visuals that are informative, expressive, and artistically satisfying.by Michal Hlavac.S.M