Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven Robot Behavior

This article develops Probabilistic Hybrid Action Models (PHAMs), a realistic causal model for predicting the behavior generated by modern percept-driven robot plans. PHAMs represent aspects of robot behavior that cannot be represented by most action models used in AI planning: the temporal structure of continuous control processes, their non-deterministic effects, several modes of their interferences, and the achievement of triggering conditions in closed-loop robot plans. The main contributions of this article are: (1) PHAMs, a model of concurrent percept-driven behavior, its formalization, and proofs that the model generates probably, qualitatively accurate predictions; and (2) a resource-efficient inference method for PHAMs based on sampling projections from probabilistic action models and state descriptions. We show how PHAMs can be applied to planning the course of action of an autonomous robot office courier based on analytical and experimental results

Prospects of a mathematical theory of human behavior in complex man-machine systems tasks

A hierarchy of human activities is derived by analyzing automobile driving in general terms. A structural description leads to a block diagram and a time-sharing computer analogy. The range of applicability of existing mathematical models is considered with respect to the hierarchy of human activities in actual complex tasks. Other mathematical tools so far not often applied to man machine systems are also discussed. The mathematical descriptions at least briefly considered here include utility, estimation, control, queueing, and fuzzy set theory as well as artificial intelligence techniques. Some thoughts are given as to how these methods might be integrated and how further work might be pursued

Software development tools: A bibliography, appendix C.

A bibliography containing approximately 200 citations on tools which help software developers perform some development task (such as text manipulation, testing, etc.), and which would not necessarily be found as part of a computing facility is given. The bibliography comes from a relatively random sampling of the literature and is not complete. But it is indicative of the nature and range of tools currently being prepared or currently available

Guidance Notes for Cloud Research Users

There is a rapidly increasing range of research activities which involve the outsourcing of computing and storage resources to public Cloud Service Providers (CSPs), who provide managed and scalable resources virtualised as a single service. For example Amazon Elastic Computing Cloud (EC2) and Simple Storage Service (S3) are two widely adopted open cloud solutions, which aim at providing pooled computing and storage services and charge users according to their weighted resource usage. Other examples include employment of Google Application Engine and Microsoft Azure as development platforms for research applications. Despite a lot of activity and publication on cloud computing, the term itself and the technologies that underpin it are still confusing to many. This note, as one of deliverables of the TeciRes project1, provides guidance to researchers who are potential end users of public CSPs for research activities. The note contains information to researchers on: •The difference between and relation to current research computing models •The considerations that have to be taken into account before moving to cloud-aided research •The issues associated with cloud computing for research that are currently being investigated •Tips and tricks when using cloud computing Readers who are interested in provisioning cloud capabilities for research should also refer to our guidance notes to cloud infrastructure service providers. This guidance notes focuses on technical aspects only. Readers who are interested in non-technical guidance should refer to the briefing paper produced by the “using cloud computing for research” project

AtomsMasher: Personal Reactive Automation for the Web

The rise of "Web 2.0" has seen an explosion of web sites for the social sharing of personal information. To enable users to make valuable use of the rich yet fragmented sea of public, social, and personal information, data mashups emerged to provide a means for combining and filtering such information into coherent feeds and visualizations. In this paper we present AtomsMasher (AM), a new framework which extends data mashups into the realm of context-aware reactive behaviors. Reactive scripts in AM can be made to trigger automatically in response to changes in its world model derived from multiple web-based data feeds. By exposing a simple state-model abstraction and query language abstractions of data derived from heterogeneous web feeds through a simulation-based interactive script debugging environment, AM greatly simplifies the process of creating such automation in a way that is flexible, predictable, scalable and within the reach of everyday Web programmers