Month: July 2019

Editorial: Statistical Relational Artificial Intelligence

Statistical Relational Artificial Intelligence (StarAI) aims at integrating logical (or relational) AI with probabilistic (or statistical) AI (De Raedt et al., 2016; Riguzzi, 2018). Relational AI achieved impressive results in structured machine learning and data mining, especially in bio- and chemo-informatics. Statistical AI is based on probabilistic (graphical) models that enable efficient reasoning and learning, and that have been applied to a wide variety of fields such as diagnosis, network communication, computational biology, computer vision, and robotics. Ultimately, StarAI may provide good starting points for developing Systems AI—the computational and mathematical modeling of complex AI systems—and in turn an engineering discipline for Artificial Intelligence and Machine Learning.

This Research Topic “Statistical Relational Artificial Intelligence” aims at presenting an overview of the latest approaches in StarAI. This topic was followed by a summer school1held in 2018 in Ferrara, Italy, as part of the series of Advanced Courses on AI (ACAI) promoted by the European Association for Artificial Intelligence.

 

Front. Robot. AI, 30 July 2019 |
Editorial: Statistical Relational Artificial Intelligence
Fabrizio Riguzzi, Kristian Kersting, Marco Lippi and Sriraam Natarajan

Source: www.frontiersin.org

Bittorio revisited: structural coupling in the Game of Life

The notion of structural coupling plays a central role in Maturana and Varela’s biology of cognition framework and strongly influenced Varela’s subsequent enactive elaboration of this framework. Building upon previous work using a glider in the Game of Life (GoL) cellular automaton as a toy model of a minimal autopoietic system with which to concretely explore these theoretical frameworks, this article presents an analysis of structural coupling between a glider and its environment. Specifically, for sufficiently small GoL universes, we completely characterize the nonautonomous dynamics of both a glider and its environment in terms of interaction graphs, derive the set of possible glider lives determined by the mutual constraints between these interaction graphs, and show how such lives are embedded in the state transition graph of the entire GoL universe.

 

Bittorio revisited: structural coupling in the Game of Life
Randall D Beer

Adaptive Behavior

Source: journals.sagepub.com

Opening for Principal Investigator (Professor or Associate Professor) Earth-Life Science Institute, Tokyo Institute of Technology

ELSI aims to answer the fundamental questions of how the Earth was formed, how life originated in the environment of early Earth, and how this life evolved into complexity. ELSI pursues these questions by studying the "origin and evolution of life" and the "origin and evolution of the Earth" through an interdisciplinary collaboration between the fields of Earth, Life, and Planetary Sciences. By understanding the early Earth context that allowed for the rise of initial life, we also work to establish a greater understanding of the likelihood of extraterrestrial life elsewhere in the universe.
We are now seeking exceptional candidates for the role of Principal Investigator (Professor or Associate Professor) to lead world-class interdisciplinary research relevant to the origin and evolution of life. ELSI works positively to eliminate biases against gender or national origin. We welcome all qualified candidates, regardless of nationality or gender. We encourage and support our candidates’ close collaborations with overseas research institutes. Our institutional language is English; Japanese language skills are not required. An unprecedented level of support for researchers to live and thrive in Japan is provided by our talented staff.

Source: www.elsi.jp

Who Is the Most Important Character in Frozen? What Networks Can Tell Us About the World

How do we determine the important characters in a movie like Frozen? We can watch it, of course, but there are also other ways—using mathematics and computers—to see who is important in the social network of a story. The idea is to compute numbers called centralities, which give ways of measuring who is important in networks. In this paper, we illustrate how different types of centralities measure importance in different ways. We also discuss how centralities are used to study many kinds of networks, not just social ones. In ongoing work, scientists are now developing centrality measures that also consider changes over time and different types of relationships.

 

Holme P, Porter M and Sayama H (2019) Who Is the Most Important Character in Frozen? What Networks Can Tell Us About the World. Front. Young Minds. 7:99. doi: 10.3389/frym.2019.00099

Source: kids.frontiersin.org