3,875 research outputs found
Approximate model composition for explanation generation
This thesis presents a framework for the formulation of knowledge models to sup¬
port the generation of explanations for engineering systems that are represented by the
resulting models. Such models are automatically assembled from instantiated generic
component descriptions, known as modelfragments. The model fragments are of suffi¬
cient detail that generally satisfies the requirements of information content as identified
by the user asking for explanations.
Through a combination of fuzzy logic based evidence preparation, which exploits the
history of prior user preferences, and an approximate reasoning inference engine, with
a Bayesian evidence propagation mechanism, different uncertainty sources can be han¬
dled. Model fragments, each representing structural or behavioural aspects of a com¬
ponent of the domain system of interest, are organised in a library. Those fragments
that represent the same domain system component, albeit with different representation
detail, form parts of the same assumption class in the library. Selected fragments are
assembled to form an overall system model, prior to extraction of any textual infor¬
mation upon which to base the explanations. The thesis proposes and examines the
techniques that support the fragment selection mechanism and the assembly of these
fragments into models.
In particular, a Bayesian network-based model fragment selection mechanism is de¬
scribed that forms the core of the work. The network structure is manually determined
prior to any inference, based on schematic information regarding the connectivity of
the components present in the domain system under consideration. The elicitation
of network probabilities, on the other hand is completely automated using probability
elicitation heuristics. These heuristics aim to provide the information required to select
fragments which are maximally compatible with the given evidence of the fragments
preferred by the user. Given such initial evidence, an existing evidence propagation
algorithm is employed. The preparation of the evidence for the selection of certain
fragments, based on user preference, is performed by a fuzzy reasoning evidence fab¬
rication engine. This engine uses a set of fuzzy rules and standard fuzzy reasoning
mechanisms, attempting to guess the information needs of the user and suggesting the selection of fragments of sufficient detail to satisfy such needs. Once the evidence
is propagated, a single fragment is selected for each of the domain system compo¬
nents and hence, the final model of the entire system is constructed. Finally, a highly
configurable XML-based mechanism is employed to extract explanation content from
the newly formulated model and to structure the explanatory sentences for the final
explanation that will be communicated to the user.
The framework is illustratively applied to a number of domain systems and is compared
qualitatively to existing compositional modelling methodologies. A further empirical
assessment of the performance of the evidence propagation algorithm is carried out to
determine its performance limits. Performance is measured against the number of frag¬
ments that represent each of the components of a large domain system, and the amount
of connectivity permitted in the Bayesian network between the nodes that stand for
the selection or rejection of these fragments. Based on this assessment recommenda¬
tions are made as to how the framework may be optimised to cope with real world
applications
Definition of an Auxiliary Processor Dedicated to Real-Time Operating System Kernels
Coordinated Science Laboratory was formerly known as Control Systems LaboratoryNASA / NAG-1-61
Content And Multimedia Database Management Systems
A database management system is a general-purpose software system that facilitates the processes of defining, constructing, and manipulating databases for various applications. The main characteristic of the ‘database approach’ is that it increases the value of data by its emphasis on data independence. DBMSs, and in particular those based on the relational data model, have been very successful at the management of administrative data in the business domain. This thesis has investigated data management in multimedia digital libraries, and its implications on the design of database management systems. The main problem of multimedia data management is providing access to the stored objects. The content structure of administrative data is easily represented in alphanumeric values. Thus, database technology has primarily focused on handling the objects’ logical structure. In the case of multimedia data, representation of content is far from trivial though, and not supported by current database management systems
Bayesian inference for protein signalling networks
Cellular response to a changing chemical environment is mediated by a complex system of interactions
involving molecules such as genes, proteins and metabolites. In particular, genetic and epigenetic variation
ensure that cellular response is often highly specific to individual cell types, or to different patients
in the clinical setting. Conceptually, cellular systems may be characterised as networks of interacting
components together with biochemical parameters specifying rates of reaction. Taken together, the network
and parameters form a predictive model of cellular dynamics which may be used to simulate the
effect of hypothetical drug regimens.
In practice, however, both network topology and reaction rates remain partially or entirely unknown,
depending on individual genetic variation and environmental conditions. Prediction under parameter
uncertainty is a classical statistical problem. Yet, doubly uncertain prediction, where both parameters
and the underlying network topology are unknown, leads to highly non-trivial probability distributions
which currently require gross simplifying assumptions to analyse. Recent advances in molecular assay
technology now permit high-throughput data-driven studies of cellular dynamics. This thesis sought to
develop novel statistical methods in this context, focussing primarily on the problems of (i) elucidating
biochemical network topology from assay data and (ii) prediction of dynamical response to therapy when
both network and parameters are uncertain
CLiFF Notes: Research in the Language, Information and Computation Laboratory of the University of Pennsylvania
One concern of the Computer Graphics Research Lab is in simulating human task behavior and understanding why the visualization of the appearance, capabilities and performance of humans is so challenging. Our research has produced a system, called Jack, for the definition, manipulation, animation and human factors analysis of simulated human figures. Jack permits the envisionment of human motion by interactive specification and simultaneous execution of multiple constraints, and is sensitive to such issues as body shape and size, linkage, and plausible motions. Enhanced control is provided by natural behaviors such as looking, reaching, balancing, lifting, stepping, walking, grasping, and so on. Although intended for highly interactive applications, Jack is a foundation for other research.
The very ubiquitousness of other people in our lives poses a tantalizing challenge to the computational modeler: people are at once the most common object around us, and yet the most structurally complex. Their everyday movements are amazingly fluid, yet demanding to reproduce, with actions driven not just mechanically by muscles and bones but also cognitively by beliefs and intentions. Our motor systems manage to learn how to make us move without leaving us the burden or pleasure of knowing how we did it. Likewise we learn how to describe the actions and behaviors of others without consciously struggling with the processes of perception, recognition, and language.
Present technology lets us approach human appearance and motion through computer graphics modeling and three dimensional animation, but there is considerable distance to go before purely synthesized figures trick our senses. We seek to build computational models of human like figures which manifest animacy and convincing behavior. Towards this end, we: Create an interactive computer graphics human model; Endow it with reasonable biomechanical properties; Provide it with human like behaviors; Use this simulated figure as an agent to effect changes in its world; Describe and guide its tasks through natural language instructions.
There are presently no perfect solutions to any of these problems; ultimately, however, we should be able to give our surrogate human directions that, in conjunction with suitable symbolic reasoning processes, make it appear to behave in a natural, appropriate, and intelligent fashion. Compromises will be essential, due to limits in computation, throughput of display hardware, and demands of real-time interaction, but our algorithms aim to balance the physical device constraints with carefully crafted models, general solutions, and thoughtful organization.
The Jack software is built on Silicon Graphics Iris 4D workstations because those systems have 3-D graphics features that greatly aid the process of interacting with highly articulated figures such as the human body. Of course, graphics capabilities themselves do not make a usable system. Our research has therefore focused on software to make the manipulation of a simulated human figure easy for a rather specific user population: human factors design engineers or ergonomics analysts involved in visualizing and assessing human motor performance, fit, reach, view, and other physical tasks in a workplace environment. The software also happens to be quite usable by others, including graduate students and animators. The point, however, is that program design has tried to take into account a wide variety of physical problem oriented tasks, rather than just offer a computer graphics and animation tool for the already computer sophisticated or skilled animator.
As an alternative to interactive specification, a simulation system allows a convenient temporal and spatial parallel programming language for behaviors. The Graphics Lab is working with the Natural Language Group to explore the possibility of using natural language instructions, such as those found in assembly or maintenance manuals, to drive the behavior of our animated human agents. (See the CLiFF note entry for the AnimNL group for details.)
Even though Jack is under continual development, it has nonetheless already proved to be a substantial computational tool in analyzing human abilities in physical workplaces. It is being applied to actual problems involving space vehicle inhabitants, helicopter pilots, maintenance technicians, foot soldiers, and tractor drivers. This broad range of applications is precisely the target we intended to reach. The general capabilities embedded in Jack attempt to mirror certain aspects of human performance, rather than the specific requirements of the corresponding workplace.
We view the Jack system as the basis of a virtual animated agent that can carry out tasks and instructions in a simulated 3D environment. While we have not yet fooled anyone into believing that the Jack figure is real , its behaviors are becoming more reasonable and its repertoire of actions more extensive. When interactive control becomes more labor intensive than natural language instructional control, we will have reached a significant milestone toward an intelligent agent
PDDL2.1: An extension of PDDL for expressing temporal planning domains
In recent years research in the planning community has moved increasingly towards application of planners to realistic problems involving both time and many types of resources. For example, interest in planning demonstrated by the space research community has inspired work in observation scheduling, planetary rover ex ploration and spacecraft control domains. Other temporal and resource-intensive domains including logistics planning, plant control and manufacturing have also helped to focus the community on the modelling and reasoning issues that must be confronted to make planning technology meet the challenges of application. The International Planning Competitions have acted as an important motivating force behind the progress that has been made in planning since 1998. The third competition (held in 2002) set the planning community the challenge of handling time and numeric resources. This necessitated the development of a modelling language capable of expressing temporal and numeric properties of planning domains. In this paper we describe the language, PDDL2.1, that was used in the competition. We describe the syntax of the language, its formal semantics and the validation of concurrent plans. We observe that PDDL2.1 has considerable modelling power --- exceeding the capabilities of current planning technology --- and presents a number of important challenges to the research community
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