456,711 research outputs found
A knowledge based software engineering environment testbed
The Carnegie Group Incorporated and Boeing Computer Services Company are developing a testbed which will provide a framework for integrating conventional software engineering tools with Artifical Intelligence (AI) tools to promote automation and productivity. The emphasis is on the transfer of AI technology to the software development process. Experiments relate to AI issues such as scaling up, inference, and knowledge representation. In its first year, the project has created a model of software development by representing software activities; developed a module representation formalism to specify the behavior and structure of software objects; integrated the model with the formalism to identify shared representation and inheritance mechanisms; demonstrated object programming by writing procedures and applying them to software objects; used data-directed and goal-directed reasoning to, respectively, infer the cause of bugs and evaluate the appropriateness of a configuration; and demonstrated knowledge-based graphics. Future plans include introduction of knowledge-based systems for rapid prototyping or rescheduling; natural language interfaces; blackboard architecture; and distributed processin
On the Configuration of More and Less Expressive Logic Programs
The decoupling between the representation of a certain problem, i.e., its
knowledge model, and the reasoning side is one of main strong points of
model-based Artificial Intelligence (AI). This allows, e.g. to focus on
improving the reasoning side by having advantages on the whole solving process.
Further, it is also well-known that many solvers are very sensitive to even
syntactic changes in the input. In this paper, we focus on improving the
reasoning side by taking advantages of such sensitivity. We consider two
well-known model-based AI methodologies, SAT and ASP, define a number of
syntactic features that may characterise their inputs, and use automated
configuration tools to reformulate the input formula or program. Results of a
wide experimental analysis involving SAT and ASP domains, taken from respective
competitions, show the different advantages that can be obtained by using input
reformulation and configuration. Under consideration in Theory and Practice of
Logic Programming (TPLP).Comment: Under consideration in Theory and Practice of Logic Programming
(TPLP
Superstructure Optimization of Naphtha Processing System with Environmental Considerations
The objective of this research project is to develop an optimization-based mathematical
model in the form of a mixed-integer linear program (MILP) for determining the optimal
configuration of a petroleum refmery. The scope for this project is to formulate the
superstructure representation model for a refinery focusing on the subsystem of naphtha
hydroprocessing in order to select the most economical and cost efficient process route.
The alternatives for all streams are evaluated and the optimal configuration is proposed
based on market demand by incorporating logical constraints and mass balance using the
GAMS modeling language platform. Based on the information and knowledge about the
physics of the problem of naphtha processing unit, we represent all these possible
processing alternatives on a superstructure. Carbon dioxide emission factors bave also
been considered in which relevant data is obtained using the carbon weighting tonne
(CWT) method. Computational studies are conducted on a representative numerical
example to illustrate the proposed modeling approach
Model-assisted validation of a strain-based dense sensor network
Recent advances in sensing are empowering the deployment of inexpensive dense sensor networks (DSNs) to conduct structural health monitoring (SHM) on large-scale structural and mechanical systems. There is a need to develop methodologies to facilitate the validation of these DSNs. Such methodologies could yield better designs of DSNs, enabling faster and more accurate monitoring of states for enhancing SHM. This paper investigates a model-assisted approach to validate a DSN of strain gauges under uncertainty. First, an approximate physical representation of the system, termed the physics-driven surrogate, is created based on the sensor network configuration. The representation consists of a state-space model, coupled with an adaptive mechanism based on sliding mode theory, to update the stiffness matrix to best match the measured responses, assuming knowledge of the mass matrix and damping parameters. Second, the physics-driven surrogate model is used to conduct a series of numerical simulations to map damages of interest to relevant features extracted from the synthetic signals that integrate uncertainties propagating through the physical representation. The capacity of the algorithm at detecting and localizing damages is quantified through probability of detection (POD) maps. It follows that such POD maps provide a direct quantification of the DSNs’ capability at conducting its SHM task. The proposed approach is demonstrated using numerical simulations on a cantilevered plate elastically restrained at the root equipped with strain gauges, where the damage of interest is a change in the root’s bending rigidity
Configuring a machining operation as a Constraint Satisfaction Problem
International audienceThe problem of configuring a machining operation is complex (many parameters and many interactions between parameters) and is generally achieved thanks to expert heuristic knowledge. Indeed, the configuration of a machining operation is often carried out according to a specific procedure: choice of a kind of operation and of a kind of machine, then choice of a set of tools and at the end selection of cutting conditions. We propose in this paper a general framework for the configuration of a machining operation based on a constraint representation and manipulation. We first present a model of the decision variables (such as the machine, the tool, the insert or the feed rate), the non-decision variable and the constraints between variables. An overview of the 32 identified constraints is given in the paper. Even though it is not exhaustive, the basic constraints of the domain are represented. A typology of the constraints to be manipulated is then given leading order to a specification of algorithms for search and consistency checking that may allow to manage these kinds of constraints
A constraint-based framework for configuration
The research presented here aims at providing a comprehensive framework for solving configuration problems, based on the Constraint Satisfaction paradigm. This thesis is addressing the two main issues raised by a configuration task: modeling the problem and solving it efficiently. Our approach subsumes previous approaches, incorporating both Simplification and further extension, offering increased representational power and efficiency.
Modeling. We advance the idea of local, context independent models for the types of objects in the application domain, and show how the model of an artifact can be built as a composition of local models of the constituent parts. Our modeling technique integrates two mechanisms for dealing with complexity, namely composition and abstraction. Using concepts such as locality, aggregation and inheritance, it offers support and guidance as to the appropriate content and organization of the domain knowledge, thus making knowledge specification and representation less error prone, and knowledge maintenance much easier.
There are two specific aspects which make modeling configuration problems challenging: the complexity and heterogeneity of relations that must be expressed, manipulated and maintained, and the dynamic nature of the configuration process. We address these issues by introducing Composite Constraint Satisfaction Problems, a new, nonstandard class of problems which extends the classic Constraint Satisfaction paradigm.
Efficiency. For the purpose of the work presented here, we are only interested in providing a guaranteed optimal solution to a configuration problem. To achieve this goal, our research focused on two complementary directions.
The first one led to a powerful search algorithm called Maintaining Arc Consistency Extended (MACE). By maintaining arc consistency and taking advantage of the problem structure, MACE turned out to be one of the best general purpose CSP search algorithms to date.
The second research direction aimed at reducing the search effort involved in proving the optimality of the proposed solution by making use of information which is specific to individual configuration problems. By adding redundant specialized constraints, the algorithm improves dramatically the lower bound computation. Using abstraction through focusing only on relevant features allows the algorithm to take advantage of context-dependent interchangeability between component instances and discard equivalent solutions, involving the same cost as solutions that have already been explored
A Distance-Geometric Method for Recovering Robot Joint Angles From an RGB Image
Autonomous manipulation systems operating in domains where human intervention
is difficult or impossible (e.g., underwater, extraterrestrial or hazardous
environments) require a high degree of robustness to sensing and communication
failures. Crucially, motion planning and control algorithms require a stream of
accurate joint angle data provided by joint encoders, the failure of which may
result in an unrecoverable loss of functionality. In this paper, we present a
novel method for retrieving the joint angles of a robot manipulator using only
a single RGB image of its current configuration, opening up an avenue for
recovering system functionality when conventional proprioceptive sensing is
unavailable. Our approach, based on a distance-geometric representation of the
configuration space, exploits the knowledge of a robot's kinematic model with
the goal of training a shallow neural network that performs a 2D-to-3D
regression of distances associated with detected structural keypoints. It is
shown that the resulting Euclidean distance matrix uniquely corresponds to the
observed configuration, where joint angles can be recovered via
multidimensional scaling and a simple inverse kinematics procedure. We evaluate
the performance of our approach on real RGB images of a Franka Emika Panda
manipulator, showing that the proposed method is efficient and exhibits solid
generalization ability. Furthermore, we show that our method can be easily
combined with a dense refinement technique to obtain superior results.Comment: IFAC 202
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