The HARPS-N Rocky Planet Search. I. HD 219134 b: A transiting rocky planet in a multi-planet system at 6.5 pc from the Sun

We know now from radial velocity surveys and transit space missions thatplanets only a few times more massive than our Earth are frequent aroundsolar-type stars. Fundamental questions about their formation history,physical properties, internal structure, and atmosphere composition are,however, still to be solved. We present here the detection of a systemof four low-mass planets around the bright (V = 5.5) and close-by (6.5pc) star HD 219134. This is the first result of the Rocky Planet Searchprogramme with HARPS-N on the Telescopio Nazionale Galileo in La Palma.The inner planet orbits the star in 3.0935 ± 0.0003 days, on aquasi-circular orbit with a semi-major axis of 0.0382 ± 0.0003AU. Spitzer observations allowed us to detect the transit of the planetin front of the star making HD 219134 b the nearest known transitingplanet to date. From the amplitude of the radial velocity variation(2.25 ± 0.22 ms-1) and observed depth of the transit(359 ± 38 ppm), the planet mass and radius are estimated to be4.36 ± 0.44 M⊕ and 1.606 ± 0.086R⊕, leading to a mean density of 5.76 ± 1.09 gcm-3, suggesting a rocky composition. One additional planetwith minimum-mass of 2.78 ± 0.65 M⊕ moves on aclose-in, quasi-circular orbit with a period of 6.767 ± 0.004days. The third planet in the system has a period of 46.66 ± 0.08days and a minimum-mass of 8.94 ± 1.13 M⊕, at0.233 ± 0.002 AU from the star. Its eccentricity is 0.46 ±0.11. The period of this planet is close to the rotational period of thestar estimated from variations of activity indicators (42.3 ± 0.1days). The planetary origin of the signal is, however, thepreferredsolution as no indication of variation at the corresponding frequency isobserved for activity-sensitive parameters. Finally, a fourth additionallonger-period planet of mass of 71 M⊕ orbits the starin 1842 days, on an eccentric orbit (e = 0.34 ± 0.17) at adistance of 2.56 AU.The photometric time series and radial velocities used in this work areavailable in electronic form at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr(ftp://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/584/A72

A Two-Stage Online Approach for Collaborative Multi-Agent Planning under Uncertainty

In a team of multiple agents, the pursuance of a common goal is a defining characteristic. Since agents may have different capabilities, and effects of actions may be uncertain, a common goal can generally only be achieved through a careful cooperation between the different agents. In this work, we propose a novel two-stage planner that combines online planning at both team level and individual level through a subgoal delegation scheme. The proposal brings the advantages of online planning approaches to the multi-agent setting. A number of modifications are made to a classical UCT approximate algorithm to (i) adapt it to the application domains considered, (ii) reduce the branching factor in the underlying search process, and (iii) effectively manage uncertain information of action effects by using information fusion mechanisms. The proposed online multi-agent planner reduces the cost of planning and decreases the temporal cost of reaching a goal, while significantly increasing the chance of success of achieving the common goal. 

Human behaviour recognition in data-scarce domains

This paper presents the novel theory for performing multi-agent activity recognition without requiring large training corpora. The reduced need for data means that robust probabilistic recognition can be performed within domains where annotated datasets are traditionally unavailable. Complex human activities are composed from sequences of underlying primitive activities. We do not assume that the exact temporal ordering of primitives is necessary, so can represent complex activity using an unordered bag. Our three-tier architecture comprises low-level video tracking, event analysis and high-level inference. High-level inference is performed using a new, cascading extension of the Rao–Blackwellised Particle Filter. Simulated annealing is used to identify pairs of agents involved in multi-agent activity. We validate our framework using the benchmarked PETS 2006 video surveillance dataset and our own sequences, and achieve a mean recognition F-Score of 0.82. Our approach achieves a mean improvement of 17% over a Hidden Markov Model baseline.

A Collaborative Multiagent Framework based on Online Risk-Aware Planning and Decision-Making

Planning is an essential process in teams of multiple agents pursuing a common goal. When the effects of actions undertaken by agents are uncertain, evaluating the potential risk of such actions alongside their utility might lead to more rational decisions upon planning. This challenge has been recently tackled for single agent settings, yet domains with multiple agents that present diverse viewpoints towards risk still necessitate comprehensive decision making mechanisms that balance the utility and risk of actions. In this work, we propose a novel collaborative multi-agent planning framework that integrates (i) a team-level online planner under uncertainty that extends the classical UCT approximate algorithm, and (ii) a preference modeling and multicriteria group decision making approach that allows agents to find accepted and rational solutions for planning problems, predicated on the attitude each agent adopts towards risk. When utilised in risk-pervaded scenarios, the proposed framework can reduce the cost of reaching the common goal sought and increase effectiveness, before making collective decisions by appropriately balancing risk and utility of actions.