Using learned action models in execution monitoring

Planners reason with abstracted models of the behaviours they use to construct plans. When plans are turned into the instructions that drive an executive, the real behaviours interacting with the unpredictable uncertainties of the environment can lead to failure. One of the challenges for intelligent autonomy is to recognise when the actual execution of a behaviour has diverged so far from the expected behaviour that it can be considered to be a failure. In this paper we present further developments of the work described in (Fox et al. 2006), where models of behaviours were learned as Hidden Markov Models. Execution of behaviours is monitored by tracking the most likely trajectory through such a learned model, while possible failures in execution are identified as deviations from common patterns of trajectories within the learned models. We present results for our experiments with a model learned for a robot behaviour

Detecting execution failures using learned action models

Planners reason with abstracted models of the behaviours they use to construct plans. When plans are turned into the instructions that drive an executive, the real behaviours interacting with the unpredictable uncertainties of the environment can lead to failure. One of the challenges for intelligent autonomy is to recognise when the actual execution of a behaviour has diverged so far from the expected behaviour that it can be considered to be a failure. In this paper we present an approach by which a trace of the execution of a behaviour is monitored by tracking its most likely explanation through a learned model of how the behaviour is normally executed. In this way, possible failures are identified as deviations from common patterns of the execution of the behaviour. We perform an experiment in which we inject errors into the behaviour of a robot performing a particular task, and explore how well a learned model of the task can detect where these errors occur

Planning with Concurrent Interacting Actions

In order to generate plans for agents with multiple actuators or agent teams, we must be able to represent and plan using concurrent actions with interacting effects. Historically, this has been considered a challenging task that could require a temporal planner. We show that, with simple modifications, the STRIPS action representation language can be used to represent concurrent interacting actions. Moreover, current algorithms for partial-order planning require only small modifications in order to handle this language and produce coordinated multiagent plans. These results open the way to partial order planners for cooperative multiagent systems. AI [8]—very little research addresses the MAP problem.2

The Old New Frontier: Studying the CERN-SPS Energy Range with NA61/SHINE

With the Large Hadron Collider entering its third year of granting us insight into the highest collision energies to date, one should nevertheless keep in mind the unexplored physics potential of lower energies. A prime example here is the NA61/SHINE experiment at the CERN Super Proton Synchrotron. Using its large-acceptance hadronic spectrometer, SHINE aims to accomplish a number of physics goals: measuring spectra of identified hadrons in hadron-nucleus collisions to provide reference for accelerator neutrino experiments and cosmic-ray observatories, investigating particle properties in the large transverse-momentum range for hadron+hadron and hadron+nucleus collisions for studying the nuclear modification factor at SPS energies, and measuring hadronic observables in a particularly interesting region of the phase diagram of strongly-interacting matter to study the onset of deconfinement and search for the critical point of strongly-interacting matter with nucleus-nucleus collisions. This contribution shall summarise results obtained so far by NA61/SHINE, as well as present the current status and plans of its experimental programme.Comment: 8 pages, 6 figures. To be published in the proceedings of the International Conference on New Frontiers in Physics (ICFP) 201

Search for critical behavior of strongly interacting matter at the CERN Super Proton Synchrotron

History, status and plans of the search for critical behavior of strongly interacting matter created in nucleus-nucleus collisions at the CERN Super Proton Synchrotron is reviewed. In particular, it is expected that the search should answer the question whether the critical point of strongly interacting matter exists and, if it does, where it is located. First, the search strategies are presented and a short introduction is given to expected fluctuation signals and to the quantities used by experiments to detect th The most important background effects are also discussed. Second, relevant experimental results are summarized and discussed. It is intriguing that both the fluctuations of quantities integrated over the full experimental acceptance (event multiplicity and transverse momentum) as well as the bin size dependence of the second factorial moment of pion and proton multiplicities in medium-sized Si+Si collisions at 158A GeV/c suggest critical behaviour of the created matter. These results provide strong motivation for the ongoing systematic scan of the phase diagram by the NA61/SHINE experiment at the SPS and the continuing search at the Brookhaven Relativistic Hadron Collider.Comment: 44 pages, 27 figures, minor text correction

First results of the ROSEBUD Dark Matter experiment

Rare Objects SEarch with Bolometers UndergrounD) is an experiment which attempts to detect low mass Weak Interacting Massive Particles (WIMPs) through their elastic scattering off Al and O nuclei. It consists of three small sapphire bolometers (of a total mass of 100 g) with NTD-Ge sensors in a dilution refrigerator operating at 20 mK in the Canfranc Underground Laboratory. We report in this paper the results of several runs (of about 10 days each) with successively improved energy thresholds, and the progressive background reduction obtained by improvement of the radiopurity of the components and subsequent modifications in the experimental assembly, including the addition of old lead shields. Mid-term plans and perspectives of the experiment are also presented.Comment: 14 pages, 8 figures, submitted to Astroparticle Physic

Decentralised governance and planning in India: case study of a tribal district

This paper examines the process of the formulation of decentralised planning in the Tribal regions of Odisha, a state located in eastern part of India, while examining the powers devolved to the local governments in such regions in the state to formulate plan, and the ground reality of the preparation of such plans in the context of the implementation of the Provisions of Panchayats (Extension to Scheduled Areas) Act (PESA Act). Formulation of decentralised planning in Odisha was taken up in the year 2008. However, based on the secondary data and interacting with the various people in field, the paper has revealed that “structural impediments” and “functional incapacity” of the local governments in the Scheduled Areas have hampered the spirit of such institutions with regard to the planning and implementation of the development programs. The paper argues that decentralised plans should be realistic, based on the effective utilisation of local resources, and the local development issues should be prioritised and implemented accordingly. The paper suggests policy measures such as effective participation, prioritisation of development needs, and rationalisation of the required and available funds, considering the significance of the PESA Act. While doing so, the issues of the tribals should receive priority

Planning with events and states

We present an overall planning system in which specifications can be described in terms of events and states. The underlying feature of this system is temporal logic, and its expressive power alloys one to deal with simultaneous actions and interacting actions. Moreover, one can represent both goal-oriented positive constraints and prevention-oriented negative constraints. The planning system can generate hierarchical plans and the overall model is capable of handling interacting agents.<br /

Accelerating Cooperative Planning for Automated Vehicles with Learned Heuristics and Monte Carlo Tree Search

Efficient driving in urban traffic scenarios requires foresight. The observation of other traffic participants and the inference of their possible next actions depending on the own action is considered cooperative prediction and planning. Humans are well equipped with the capability to predict the actions of multiple interacting traffic participants and plan accordingly, without the need to directly communicate with others. Prior work has shown that it is possible to achieve effective cooperative planning without the need for explicit communication. However, the search space for cooperative plans is so large that most of the computational budget is spent on exploring the search space in unpromising regions that are far away from the solution. To accelerate the planning process, we combined learned heuristics with a cooperative planning method to guide the search towards regions with promising actions, yielding better solutions at lower computational costs