6,454 research outputs found
Modeling time and valuation in structured argumentation frameworks
Temporal Argumentation Frameworks (TAF) represent a recent extension of Dung's abstract argumentation frameworks that consider the temporal availability of arguments. In a TAF, arguments are valid during specific time intervals, called availability intervals, while the attack relation of the framework remains static and permanent in time; thus, in general, when identifying the set of acceptable arguments, the outcome associated with a TAF will vary in time. We introduce an extension of TAF, called Extended Temporal Argumentation Framework (E-TAF), adding the capability of modeling the temporal availability of attacks among arguments, thus modeling special features of arguments varying over time and the possibility that attacks are only available in a given time interval. E-TAF will be enriched by considering Structured Abstract Argumentation, using Dynamic Argumentation Frameworks. The resulting framework, E-TAF∗, provides a suitable model for different time-dependent issues satisfying properties and equivalence results that permit to contrast the expressivity of E-TAF and E-TAF∗ with argumentation based on abstract frameworks. Thus, the main contribution here is to provide an enhanced framework for modeling special features of argumentation varying over time, which are relevant in many real-world situations. The proposal aims at advancing in the integration of time and valuation in the context of argumentation systems as well.Fil: Budan, Maximiliano Celmo David. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional de Santiago del Estero. Facultad de Ciencias Exactas y Tecnologías. Departamento de Matemática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; ArgentinaFil: Gomez Lucero, Mauro Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; ArgentinaFil: Chesñevar, Carlos Iván. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional de Santiago del Estero. Facultad de Ciencias Exactas y Tecnologías. Departamento de Matemática; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; ArgentinaFil: Simari, Guillermo Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentin
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Neurons and symbols: a manifesto
We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of
neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty
Abstract Argumentation / Persuasion / Dynamics
The act of persuasion, a key component in rhetoric argumentation, may be
viewed as a dynamics modifier. We extend Dung's frameworks with acts of
persuasion among agents, and consider interactions among attack, persuasion and
defence that have been largely unheeded so far. We characterise basic notions
of admissibilities in this framework, and show a way of enriching them through,
effectively, CTL (computation tree logic) encoding, which also permits
importation of the theoretical results known to the logic into our
argumentation frameworks. Our aim is to complement the growing interest in
coordination of static and dynamic argumentation.Comment: Arisaka R., Satoh K. (2018) Abstract Argumentation / Persuasion /
Dynamics. In: Miller T., Oren N., Sakurai Y., Noda I., Savarimuthu B., Cao
Son T. (eds) PRIMA 2018: Principles and Practice of Multi-Agent Systems.
PRIMA 2018. Lecture Notes in Computer Science, vol 11224. Springer, Cha
Dimensions of Neural-symbolic Integration - A Structured Survey
Research on integrated neural-symbolic systems has made significant progress
in the recent past. In particular the understanding of ways to deal with
symbolic knowledge within connectionist systems (also called artificial neural
networks) has reached a critical mass which enables the community to strive for
applicable implementations and use cases. Recent work has covered a great
variety of logics used in artificial intelligence and provides a multitude of
techniques for dealing with them within the context of artificial neural
networks. We present a comprehensive survey of the field of neural-symbolic
integration, including a new classification of system according to their
architectures and abilities.Comment: 28 page
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Value-based argumentation frameworks as neural-symbolic learning systems
While neural networks have been successfully used in a number of machine learning applications, logical languages have been the standard for the representation of argumentative reasoning. In this paper, we establish a relationship between neural networks and argumentation networks, combining reasoning and learning in the same argumentation framework. We do so by presenting a new neural argumentation algorithm, responsible for translating argumentation networks into standard neural networks. We then show a correspondence between the two networks. The algorithm works not only for acyclic argumentation networks, but also for circular networks, and it enables the accrual of arguments through learning as well as the parallel computation of arguments
Engineering simulations for cancer systems biology
Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a framework that supports developing simulations as scientific instruments, and we select cancer systems biology as an exemplar domain, with a particular focus on cellular signalling models. We consider the challenges of lack of data, incomplete knowledge and modelling in the context of a rapidly changing knowledge base. Our framework comprises a process to clearly separate scientific and engineering concerns in model and simulation development, and an argumentation approach to documenting models for rigorous way of recording assumptions and knowledge gaps. We propose interactive, dynamic visualisation tools to enable the biological community to interact with cellular signalling models directly for experimental design. There is a mismatch in scale between these cellular models and tissue structures that are affected by tumours, and bridging this gap requires substantial computational resource. We present concurrent programming as a technology to link scales without losing important details through model simplification. We discuss the value of combining this technology, interactive visualisation, argumentation and model separation to support development of multi-scale models that represent biologically plausible cells arranged in biologically plausible structures that model cell behaviour, interactions and response to therapeutic interventions
Towards an Indexical Model of Situated Language Comprehension for Cognitive Agents in Physical Worlds
We propose a computational model of situated language comprehension based on
the Indexical Hypothesis that generates meaning representations by translating
amodal linguistic symbols to modal representations of beliefs, knowledge, and
experience external to the linguistic system. This Indexical Model incorporates
multiple information sources, including perceptions, domain knowledge, and
short-term and long-term experiences during comprehension. We show that
exploiting diverse information sources can alleviate ambiguities that arise
from contextual use of underspecific referring expressions and unexpressed
argument alternations of verbs. The model is being used to support linguistic
interactions in Rosie, an agent implemented in Soar that learns from
instruction.Comment: Advances in Cognitive Systems 3 (2014
About Norms and Causes
Knowing the norms of a domain is crucial, but there exist no repository of
norms. We propose a method to extract them from texts: texts generally do not
describe a norm, but rather how a state-of-affairs differs from it. Answers
concerning the cause of the state-of-affairs described often reveal the
implicit norm. We apply this idea to the domain of driving, and validate it by
designing algorithms that identify, in a text, the "basic" norms to which it
refers implicitly
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Arguing satisfaction of security requirements
This chapter presents a process for security requirements elicitation and analysis,
based around the construction of a satisfaction argument for the security of a
system. The process starts with the enumeration of security goals based on assets
in the system, then uses these goals to derive security requirements in the form of
constraints. Next, a satisfaction argument for the system is constructed, using a
problem-centered representation, a formal proof to analyze properties that can be
demonstrated, and structured informal argumentation of the assumptions exposed
during construction of the argument. Constructing the satisfaction argument can
expose missing and inconsistent assumptions about system context and behavior
that effect security, and a completed argument provides assurances that a system
can respect its security requirements
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