Virtual Reality Art involves the design of artificial worlds that offer new experiences to spectators. An important aspect for the development of VR Art installations is the principled definition of behaviour for the environment as a whole, which would facilitate experiments with alternative laws of physics, time, and causality. We describe the first results of an ongoing project dedicated to the development of software tools for the use of Intelligent Virtual Environments in VR Art. Using the architecture of a state-of-theart game engine, we have developed Artificial Intelligence techniques that support the definition of alternative laws of physics. After discussing the principles behind alternative reality we describe two complementary modes of description for alternative behaviour: qualitative physics and causal simulation. This is illustrated by examples integrated into the virtual environment.

In this paper I give a brief overview of recent work on uncertainty in AI, and relate it to logical representations. Bayesian decision theory and logic are both normative frameworks for reasoning that emphasize different aspects of intelligent reasoning. Belief networks (Bayesian networks) are representations of independence that form the basis for understanding much of the recent work on reasoning under uncertainty, evidential and causal reasoning, decision analysis, dynamical systems, optimal control, reinforcement learning and Bayesian learning. The independent choice logic provides a bridge between logical representations and belief networks that lets us understand these other representations and their relationship to logic and shows how they can extended to first-order rule-based representations.

and human/machine learning

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. A conceptual framework whose goal is the efficiency of machine learning is proposed and justified. The framework is designed in a broader context, that of problem solver (PS). The design is solved as an integration of all basic cognitive functions and as a software-engineering problem. Many (one hundred) requirements imposed on PS are considered. The most important are the object-oriented nature of the PS environment, reflexivity of PS, and central role of tool and shifted border. 1 INTRODUCTION In the paper, a framework for machine learning is proposed. It is a new framework 2 , even if it is "99%-based" on known concepts, based on their integration. There are a lot of them. They are unified in one super-principle (see later). The sophisticated robots on RoboCup-99 did not demonstrate a very intelligent game. This was not because they were not able to form teams of autonomous robots, demonstrate multi-source fusion of perception, and use multi-agent game theory. I contrast thes...

It is well know the necessity of managing relationships among agents in a multi-agent system to achieve coordinated behavior. One approach to manage such relationships consists of using an explicit representation of them, allowing each agent to choose its actions based on them. Previous work in the area have considered ideal situations, such as fully known environments, static relationships and shared mental states. In this paper we propose to represent relationships among agents and entities in a multi-agent system by using influence diagrams.

Reconstructing the sequence of computer events that led to a particular event is an essential part of the digital investigation process. The ability to quantify the accuracy of automatic event reconstruction systems is an essential step in standardizing the digital investigation process thereby making it resilient to tactics such as the Trojan Horse defense. In this paper, we present findings from an empirical study to measure and compare the accuracy and effectiveness of a suite of such event reconstruction techniques. We quantify (as applicable) the rates of false positives, false negatives, and scalability both in terms of computational burden and memory-usage. Some of our findings are quite surprising in the sense of not matching a priori expectations, and whereas other findings qualitatively match the a priori expectations they were never before quantitatively put to the test to determine the boundaries of their applicability. For example, our results show that automatic event reconstruction systems proposed in literature have very high false-positive rates (up to %). 1

Scientific reasoning is a complex process, alternately requiring flashes of insight and tedious analysis. This dichotomy is evident in constructing a geologic timeline for a landform using cosmogenic isotope dating. Experts in this field frequently spend months on repetitive mathematical tasks, until they have gathered enough information to suddenly understand the

In this paper, we will review a novel microscopy modality called Knife-Edge Scanning Microscopy (KESM) that we have developed over the past twelve years (since 1999) and discuss its relevance to connectomics and neural networks research. The operational principle of KESM is to simultaneously section and image small animal brains embedded in hard polymer resin so that a near-isotropic, sub-micrometer voxel size of 0.6 µm × 0.7 µm × 1.0 µm can be achieved over ∼1 cm 3 volume of tissue which is enough to hold an entire mouse brain. At this resolution, morphological details such as dendrites, dendritic spines, and axons are visible (for sparse stains like Golgi). KESM has been successfully used to scan whole mouse brains stained in Golgi (neuronal morphology), Nissl (somata), and India ink (vasculature), providing unprecedented insights into the system-level architectural layout of microstructures within the mouse brain. In this paper, we will present whole-brain-scale data sets from KESM and discuss challenges and opportunities posed to connectomics and neural networks research by such detailed yet system-level data. I.