813 research outputs found
Constructing Real-Time Systems from Temporal I/O Automata
A new class of communicating automata called Temporal Input/Output Automata (TAi/os) is introduced. A TAi/o is a predicate automaton used to specify real-time systems. The specification provided by a TAi/o includes state predicates with proof expressions and abstract program syntax as attributes. An abstract program is extracted during a constructive proof of the specification using the proof expressions. A TAi/o specification also includes hard, real-time constraints on program behavior. The predictability of deterministic, temporally complete TAi/o is investigated. The formulation of real-time system transductions and transduction rules for TAi/os in explicit clock temporal logic is given. An illustration of the use of TAi/os in specifying light-controlled vehicles is presented. To illustrate the methodology in constructive reasoning about a TAi/o, a proof which derives a partial abstract program is given
COEL: A Web-based Chemistry Simulation Framework
The chemical reaction network (CRN) is a widely used formalism to describe
macroscopic behavior of chemical systems. Available tools for CRN modelling and
simulation require local access, installation, and often involve local file
storage, which is susceptible to loss, lacks searchable structure, and does not
support concurrency. Furthermore, simulations are often single-threaded, and
user interfaces are non-trivial to use. Therefore there are significant hurdles
to conducting efficient and collaborative chemical research. In this paper, we
introduce a new enterprise chemistry simulation framework, COEL, which
addresses these issues. COEL is the first web-based framework of its kind. A
visually pleasing and intuitive user interface, simulations that run on a large
computational grid, reliable database storage, and transactional services make
COEL ideal for collaborative research and education. COEL's most prominent
features include ODE-based simulations of chemical reaction networks and
multicompartment reaction networks, with rich options for user interactions
with those networks. COEL provides DNA-strand displacement transformations and
visualization (and is to our knowledge the first CRN framework to do so), GA
optimization of rate constants, expression validation, an application-wide
plotting engine, and SBML/Octave/Matlab export. We also present an overview of
the underlying software and technologies employed and describe the main
architectural decisions driving our development. COEL is available at
http://coel-sim.org for selected research teams only. We plan to provide a part
of COEL's functionality to the general public in the near future.Comment: 23 pages, 12 figures, 1 tabl
An Introduction to Rule-based Modeling of Immune Receptor Signaling
Cells process external and internal signals through chemical interactions.
Cells that constitute the immune system (e.g., antigen presenting cell, T-cell,
B-cell, mast cell) can have different functions (e.g., adaptive memory,
inflammatory response) depending on the type and number of receptor molecules
on the cell surface and the specific intracellular signaling pathways activated
by those receptors. Explicitly modeling and simulating kinetic interactions
between molecules allows us to pose questions about the dynamics of a signaling
network under various conditions. However, the application of chemical kinetics
to biochemical signaling systems has been limited by the complexity of the
systems under consideration. Rule-based modeling (BioNetGen, Kappa, Simmune,
PySB) is an approach to address this complexity. In this chapter, by
application to the FcRI receptor system, we will explore the
origins of complexity in macromolecular interactions, show how rule-based
modeling can be used to address complexity, and demonstrate how to build a
model in the BioNetGen framework. Open source BioNetGen software and
documentation are available at http://bionetgen.org.Comment: 5 figure
Modelling learning behaviour of intelligent agents using UML 2.0
This thesis aims to explore and demonstrate the ability of the new standard of
structural and behavioural components in Unified Modelling Language (UML 2.0 / 2004)
to model the learning behaviour of Intelligent Agents. The thesis adopts the research
direction that views agent-oriented systems as an extension to object-oriented systems. In
view of the fact that UML has been the de facto standard for modelling object-oriented
systems, this thesis concentrates on exploring such modelling potential with Intelligent
Agent-oriented systems. Intelligent Agents are Agents that have the capability to learn and
reach agreement with other Agents or users. The research focuses on modelling the
learning behaviour of a single Intelligent Agent, as it is the core of multi-agent systems.
During the writing of the thesis, the only work done to use UML 2.0 to model
structural components of Agents was from the Foundation for Intelligent Physical Agent
(FIPA). The research builds upon, explores, and utilises this work and provides further
development to model the structural components of learning behaviour of Intelligent
Agents. The research also shows the ability of UML version 2.0 behaviour diagrams,
namely activity diagrams and sequence diagrams, to model the learning behaviour of
Intelligent Agents that use learning from observation and discovery as well as learning
from examples of strategies. The research also evaluates if UML 2.0 state machine
diagrams can model specific reinforcement learning algorithms, namely dynamic
programming, Monte Carlo, and temporal difference algorithms. The thesis includes user
guides of UML 2.0 activity, sequence, and state machine diagrams to allow researchers in
agent-oriented systems to use the UML 2.0 diagrams in modelling the learning components
of Intelligent Agents.
The capacity for learning is a crucial feature of Intelligent Agents. The research
identifies different learning components required to model the learning behaviour of
Intelligent Agents such as learning goals, learning strategies, and learning feedback
methods. In recent years, the Agent-oriented research has been geared towards the agency
dimension of Intelligent Agents. Thus, there is a need to conduct more research on the
intelligence dimension of Intelligent Agents, such as negotiation and argumentation skills.
The research shows that behavioural components of UML 2.0 are capable of
modelling the learning behaviour of Intelligent Agents while structural components of
UML 2.0 need extension to cover structural requirements of Agents and Intelligent Agents.
UML 2.0 has an extension mechanism to fulfil Agents and Intelligent Agents for such
requirements. This thesis will lead to increasing interest in the intelligence dimension
rather than the agency dimension of Intelligent Agents, and pave the way for objectoriented
methodologies to shift more easily to paradigms of Intelligent Agent-oriented
systems.The British
Council, the University of Plymouth and the Arab-British Chamber Charitable Foundation
A UML-integrated test description language for component testing
International audienceA mass market in reusable components demands a high level of component quality, testing being a crucial part of software quality assurance. For components modelled in UML there are significant advantages to using UML also for the test description language. Since we wish to describe tests of non-trivial temporal ordering properties, we define our test description language based around UML interaction diagrams, seeking inspiration from the work on conformance testing of telecom protocols. We aim at a fully integrated approach which can be captured in a UML component testing profile
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