5,424 research outputs found
Semantics, Modelling, and the Problem of Representation of Meaning -- a Brief Survey of Recent Literature
Over the past 50 years many have debated what representation should be used
to capture the meaning of natural language utterances. Recently new needs of
such representations have been raised in research. Here I survey some of the
interesting representations suggested to answer for these new needs.Comment: 15 pages, no figure
The 1990 progress report and future plans
This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers
Recognising high-level agent behaviour through observations in data scarce domains
This thesis presents a novel method for performing multi-agent behaviour 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 (e.g. surveillance, defence). Human behaviours are composed
from sequences of underlying activities that can be used as salient features. We do not
assume that the exact temporal ordering of such features is necessary, so can represent
behaviours using an unordered “bag-of-features”. A weak temporal ordering is imposed
during inference to match behaviours to observations and replaces the learnt model parameters
used by competing methods. 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. Behaviours are
recognised at multiple levels of abstraction and can contain a mixture of solo and multiagent
behaviour. We validate our framework using the PETS 2006 video surveillance
dataset and our own video sequences, in addition to a large corpus of simulated data.
We achieve a mean recognition precision of 96.4% on the simulated data and 89.3% on
the combined video data. Our “bag-of-features” framework is able to detect when behaviours
terminate and accurately explains agent behaviour despite significant quantities
of low-level classification errors in the input, and can even detect agents who change their
behaviour
Modeling Option and Strategy Choices with Connectionist Networks: Towards an Integrative Model of Automatic and Deliberate Decision Making
We claim that understanding human decisions requires that both automatic and deliberate processes be considered. First, we sketch the qualitative differences between two hypothetical processing systems, an automatic and a deliberate system. Second, we show the potential that connectionism offers for modeling processes of decision making and discuss some empirical evidence. Specifically, we posit that the integration of information and the application of a selection rule are governed by the automatic system. The deliberate system is assumed to be responsible for information search, inferences and the modification of the network that the automatic processes act on. Third, we critically evaluate the multiple-strategy approach to decision making. We introduce the basic assumption of an integrative approach stating that individuals apply an all-purpose rule for decisions but use different strategies for information search. Fourth, we develop a connectionist framework that explains the interaction between automatic and deliberate processes and is able to account for choices both at the option and at the strategy level.System 1, Intuition, Reasoning, Control, Routines, Connectionist Model, Parallel Constraint Satisfaction
CBR and MBR techniques: review for an application in the emergencies domain
The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system.
RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to:
a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions
b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location.
In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations.
This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version
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