Characteristic Evolution and Matching

I review the development of numerical evolution codes for general relativity based upon the characteristic initial value problem. Progress in characteristic evolution is traced from the early stage of 1D feasibility studies to 2D axisymmetric codes that accurately simulate the oscillations and gravitational collapse of relativistic stars and to current 3D codes that provide pieces of a binary black hole spacetime. Cauchy codes have now been successful at simulating all aspects of the binary black hole problem inside an artificially constructed outer boundary. A prime application of characteristic evolution is to extend such simulations to null infinity where the waveform from the binary inspiral and merger can be unambiguously computed. This has now been accomplished by Cauchy-characteristic extraction, where data for the characteristic evolution is supplied by Cauchy data on an extraction worldtube inside the artificial outer boundary. The ultimate application of characteristic evolution is to eliminate the role of this outer boundary by constructing a global solution via Cauchy-characteristic matching. Progress in this direction is discussed.Comment: New version to appear in Living Reviews 2012. arXiv admin note: updated version of arXiv:gr-qc/050809

Socio-demographic factors associated with pet ownership amongst adolescents from a UK birth cohort

Background: In developed nations, pet ownership is common within families. Both physical and psychological health benefits may result from owning a pet during childhood and adolescence. However, it is difficult to determine whether these benefits are due to pet ownership directly or to factors linked to both pet ownership and health. Previous research found associations between a range of socio-demographic factors and pet ownership in seven-year-old children from a UK cohort. The current study extends this research to adolescence, considering that these factors may be important to consider in future Human-Animal Interaction (HAI) research across childhood.Results:The Avon Longitudinal Study of Parents and Children (ALSPAC) collected pet ownership data prospectively via maternal reports from gestation up to age 10 years old and via self-report retrospectively at age 18 for ages 11(n= 3063) to 18 years old (n= 3098) on cats, dogs, rabbits, rodents, birds, fish, tortoise/turtles and horses. The dataset also contains a wide range of potential confounders, including demographic and socio-economic variables.The ownership of all pet types peaked at age 11 (80%) and then decreased during adolescence, with the exclusion of cats which remained constant (around 30%), and dogs which increased through 11–18 years (26–37%). Logistic regression was used to build multivariable models for ownership of each pet type at age 13 years, and the factors identified in these models were compared to previously published data for 7 year-olds in the same cohort. There was some consistency with predictors reported at age 7. Generally sex, birth order, maternal age, maternal education, number of people in the household, house type, and concurrent ownership of other pets were associated with pet ownership at both 7 and 13 years (the direction of association varied according to pet type).Factors that were no longer associated with adolescent pet ownership included child ethnicity, paternal education,and parental social class.Conclusions:A number of socio-demographic factors are associated with pet ownership in childhood and adolescence and they differ according to the type of pet, and age of child. These factors are potential confounders that must be considered in future HAI studies

Characteristic Evolution and Matching

I review the development of numerical evolution codes for general relativity based upon the characteristic initial value problem. Progress is traced from the early stage of 1D feasibility studies to 2D axisymmetric codes that accurately simulate the oscillations and gravitational collapse of relativistic stars and to current 3D codes that provide pieces of a binary black spacetime. A prime application of characteristic evolution is to compute waveforms via Cauchy-characteristic matching, which is also reviewed.Comment: Published version http://www.livingreviews.org/lrr-2005-1

Learning about the opponent in automated bilateral negotiation: a comprehensive survey of opponent modeling techniques

A negotiation between agents is typically an incomplete information game, where the agents initially do not know their opponent’s preferences or strategy. This poses a challenge, as efficient and effective negotiation requires the bidding agent to take the other’s wishes and future behavior into account when deciding on a proposal. Therefore, in order to reach better and earlier agreements, an agent can apply learning techniques to construct a model of the opponent. There is a mature body of research in negotiation that focuses on modeling the opponent, but there exists no recent survey of commonly used opponent modeling techniques. This work aims to advance and integrate knowledge of the field by providing a comprehensive survey of currently existing opponent models in a bilateral negotiation setting. We discuss all possible ways opponent modeling has been used to benefit agents so far, and we introduce a taxonomy of currently existing opponent models based on their underlying learning techniques. We also present techniques to measure the success of opponent models and provide guidelines for deciding on the appropriate performance measures for every opponent model type in our taxonomy

Computers that negotiate on our behalf: Major challenges for self-sufficient, self-directed, and interdependent negotiating agents

Computers that negotiate on our behalf hold great promise for the future and will even become indispensable in emerging application domains such as the smart grid, autonomous driving, and the Internet of Things. Much research has thus been expended to create agents that are able to negotiate in an abundance of circumstances. However, up until now, truly autonomous negotiators have rarely been deployed in real-world applications. This paper sizes up current negotiating agents and explores a number of technological, societal and ethical challenges that autonomous negotiation systems are bringing about. The questions we address are: in what sense are these systems autonomous, what has been holding back their further proliferation, and is their spread something we should encourage? We relate the automated negotiation research agenda to dimensions of autonomy and distill three major themes that we believe will propel autonomous negotiation forward: accurate representation, long-term perspective, and user trust. We argue these orthogonal research directions need to be aligned and advanced in unison to sustain tangible progress in the field