37,591 research outputs found
A design model for Open Distributed Processing systems
This paper proposes design concepts that allow the conception, understanding and development of complex technical structures for open distributed systems. The proposed concepts are related to, and partially motivated by, the present work on Open Distributed Processing (ODP). As opposed to the current ODP approach, the concepts are aimed at supporting a design trajectory with several, related abstraction levels. Simple examples are used to illustrate the proposed concepts
Closed Terminologies and Temporal Reasoning in Description Logic for Concept and Plan Recognition
Description logics are knowledge representation formalisms in the tradition of frames and semantic networks, but with an emphasis on formal semantics. A terminology contains descriptions of concepts, such as UNIVERSITY, which are automatically classified ina taxonomy via subsumption inferences. Individuals such as COLUMBIA are described in terms of those concepts. This thesis enhances the scope and utility of description logics by exploiting new completeness assumptions during problem solving and by extending the expressiveness of descriptions. First, we introduce a predictive concept recognition methodology based on a new closed terminology assumption (CTA). The terminology is dynamically partitioned by modalities (necessary, optional, and impossible) with respect to individuals as they are specified. In our interactive configuration application, a user incrementally specifies an individual computer system and its components in collaboration with a configuration engine. Choices can be made in any order and at any level of abstraction. We distinguish between abstract and concrete concepts to formally define when an individual's description may be considered finished. We also exploit CTA, together with the terminology's subsumption-based organization, to efficiently track the types of systems and components consistent with current choices, infer additional constraints on current choices, and appropriately restrict future choices. Thus, we can help focus the efforts of both user and configuration engine. This work is implemented in the K-REP system. Second, we show that a new class of complex descriptions can be formed via constraint networks over standard descriptions. For example, we model plans as constraint networks whose nodes represent actions.Arcs represent qualitative and metric temporal constraints, plusco-reference constraints, between actions. By combining terminological reasoning with constraint satisfaction techniques, subsumption is extended to constraint networks, allowing automatic classification of a plan library. This work is implemented in the T-REX system, which integrates and builds upon an existing description logic system (K-REP or CLASSIC) and temporal reasoner (MATS). Finally, we combine the preceding, orthogonal results to conduct predictive recognition of constraint network concepts. As an example,this synthesis enables a new approach to deductive plan recognition,illustrated with travel plans. This work is also realized in T-REX
Geospatial Narratives and their Spatio-Temporal Dynamics: Commonsense Reasoning for High-level Analyses in Geographic Information Systems
The modelling, analysis, and visualisation of dynamic geospatial phenomena
has been identified as a key developmental challenge for next-generation
Geographic Information Systems (GIS). In this context, the envisaged
paradigmatic extensions to contemporary foundational GIS technology raises
fundamental questions concerning the ontological, formal representational, and
(analytical) computational methods that would underlie their spatial
information theoretic underpinnings.
We present the conceptual overview and architecture for the development of
high-level semantic and qualitative analytical capabilities for dynamic
geospatial domains. Building on formal methods in the areas of commonsense
reasoning, qualitative reasoning, spatial and temporal representation and
reasoning, reasoning about actions and change, and computational models of
narrative, we identify concrete theoretical and practical challenges that
accrue in the context of formal reasoning about `space, events, actions, and
change'. With this as a basis, and within the backdrop of an illustrated
scenario involving the spatio-temporal dynamics of urban narratives, we address
specific problems and solutions techniques chiefly involving `qualitative
abstraction', `data integration and spatial consistency', and `practical
geospatial abduction'. From a broad topical viewpoint, we propose that
next-generation dynamic GIS technology demands a transdisciplinary scientific
perspective that brings together Geography, Artificial Intelligence, and
Cognitive Science.
Keywords: artificial intelligence; cognitive systems; human-computer
interaction; geographic information systems; spatio-temporal dynamics;
computational models of narrative; geospatial analysis; geospatial modelling;
ontology; qualitative spatial modelling and reasoning; spatial assistance
systemsComment: ISPRS International Journal of Geo-Information (ISSN 2220-9964);
Special Issue on: Geospatial Monitoring and Modelling of Environmental
Change}. IJGI. Editor: Duccio Rocchini. (pre-print of article in press
Case Adaptation with Qualitative Algebras
This paper proposes an approach for the adaptation of spatial or temporal
cases in a case-based reasoning system. Qualitative algebras are used as
spatial and temporal knowledge representation languages. The intuition behind
this adaptation approach is to apply a substitution and then repair potential
inconsistencies, thanks to belief revision on qualitative algebras. A temporal
example from the cooking domain is given. (The paper on which this extended
abstract is based was the recipient of the best paper award of the 2012
International Conference on Case-Based Reasoning.
A provably correct MPC approach to safety control of urban traffic networks
Model predictive control (MPC) is a popular strategy for urban traffic management that is able to incorporate physical and user defined constraints. However, the current MPC methods rely on finite horizon predictions that are unable to guarantee desirable behaviors over long periods of time. In this paper we design an MPC strategy that is guaranteed to keep the evolution of a network in a desirable yet arbitrary -safe- set, while optimizing a finite horizon cost function. Our approach relies on finding a robust controlled invariant set inside the safe set that provides an appropriate terminal constraint for the MPC optimization problem. An illustrative example is included.This work was partially supported by the NSF under grants CPS-1446151 and CMMI-1400167. (CPS-1446151 - NSF; CMMI-1400167 - NSF
Using temporal abduction for biosignal interpretation: A case study on QRS detection
In this work, we propose an abductive framework for biosignal interpretation,
based on the concept of Temporal Abstraction Patterns. A temporal abstraction
pattern defines an abstraction relation between an observation hypothesis and a
set of observations constituting its evidence support. New observations are
generated abductively from any subset of the evidence of a pattern, building an
abstraction hierarchy of observations in which higher levels contain those
observations with greater interpretative value of the physiological processes
underlying a given signal. Non-monotonic reasoning techniques have been applied
to this model in order to find the best interpretation of a set of initial
observations, permitting even to correct these observations by removing, adding
or modifying them in order to make them consistent with the available domain
knowledge. Some preliminary experiments have been conducted to apply this
framework to a well known and bounded problem: the QRS detection on ECG
signals. The objective is not to provide a new better QRS detector, but to test
the validity of an abductive paradigm. These experiments show that a knowledge
base comprising just a few very simple rhythm abstraction patterns can enhance
the results of a state of the art algorithm by significantly improving its
detection F1-score, besides proving the ability of the abductive framework to
correct both sensitivity and specificity failures.Comment: 7 pages, Healthcare Informatics (ICHI), 2014 IEEE International
Conference o
Parameterized Synthesis
We study the synthesis problem for distributed architectures with a
parametric number of finite-state components. Parameterized specifications
arise naturally in a synthesis setting, but thus far it was unclear how to
detect realizability and how to perform synthesis in a parameterized setting.
Using a classical result from verification, we show that for a class of
specifications in indexed LTL\X, parameterized synthesis in token ring networks
is equivalent to distributed synthesis in a network consisting of a few copies
of a single process. Adapting a well-known result from distributed synthesis,
we show that the latter problem is undecidable. We describe a semi-decision
procedure for the parameterized synthesis problem in token rings, based on
bounded synthesis. We extend the approach to parameterized synthesis in
token-passing networks with arbitrary topologies, and show applicability on a
simple case study. Finally, we sketch a general framework for parameterized
synthesis based on cutoffs and other parameterized verification techniques.Comment: Extended version of TACAS 2012 paper, 29 page
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