104,843 research outputs found
HIERARCHICAL-GRANULARITY HOLONIC MODELLING
This thesis aims to introduce an agent-based system engineering approach,
named Hierarchical-Granularity Holonic Modelling, to support intelligent
information processing at multiple granularity levels. The focus is especially
on complex hierarchical systems.
Nowadays, due to ever growing complexity of information systems and
processes, there is an increasing need of a simple self-modular computational
model able to manage data and perform information granulation at different
resolutions (i.e., both spatial and temporal). The current literature lacks to
provide such a methodology. To cite a relevant example, the object-oriented
paradigm is suitable for describing a system at a given representation level;
notwithstanding, further design effort is needed if a more synthetical of more
analytical view of the same system is required.
In the literature, the agent paradigm represents a viable solution in complex
systems modelling; in particular, Multi-Agent Systems have been applied with
success in a countless variety of distributed intelligence settings. Current
agent-oriented implementations however suffer from an apparent dichotomy
between agents as intelligent entities and agents\u2019 structures as superimposed
hierarchies of roles within a given organization. The agents\u2019 architectures are
often rigid and require intense re-engineering when the underpinning ontology
is updated to cast new design criteria.
The latest stage in the evolution of modelling frameworks is represented by
Holonic Systems, based on the notion of \u2018holon\u2019 and \u2018holarchy\u2019 (i.e.,
hierarchy of holons). A holon, just like an agent, is an intelligent entity able to
interact with the environment and to take decisions to solve a specific
problem. Contrarily to agent, holon has the noteworthy property of playing the
role of a whole and a part at the same time. This reflects at the organizational
level: holarchy functions first as autonomous wholes in supra-ordination to
their parts, secondly as dependent parts in sub-ordination to controls on higher
levels, and thirdly in coordination with their local environment.
These ideas were originally devised by Arthur Koestler in 1967. Since then,
Holonic Systems have gained more and more credit in various fields such as
Biology, Ecology, Theory of Emergence and Intelligent Manufacturing.
Notwithstanding, with respect to these disciplines, fewer works on Holonic
Systems can be found in the general framework of Artificial and
Computational Intelligence. Moreover, the distance between theoretic models
and actual implementation is still wide open.
In this thesis, starting from the Koestler\u2019s original idea, we devise a novel
agent-inspired model that merges intelligence with the holonic structure at
multiple hierarchical-granularity levels. This is made possible thanks to a rule-based
knowledge recursive representation, which allows the holonic agent to
carry out both operating and learning tasks in a hierarchy of granularity levels.
The proposed model can be directly used in terms of hardware/software
applications. This endows systems and software engineers with a modular and
scalable approach when dealing with complex hierarchical systems. In order
to support our claims, exemplar experiments of our proposal are shown and
prospective implications are commented
Multi-level Memory for Task Oriented Dialogs
Recent end-to-end task oriented dialog systems use memory architectures to
incorporate external knowledge in their dialogs. Current work makes simplifying
assumptions about the structure of the knowledge base, such as the use of
triples to represent knowledge, and combines dialog utterances (context) as
well as knowledge base (KB) results as part of the same memory. This causes an
explosion in the memory size, and makes the reasoning over memory harder. In
addition, such a memory design forces hierarchical properties of the data to be
fit into a triple structure of memory. This requires the memory reader to infer
relationships across otherwise connected attributes. In this paper we relax the
strong assumptions made by existing architectures and separate memories used
for modeling dialog context and KB results. Instead of using triples to store
KB results, we introduce a novel multi-level memory architecture consisting of
cells for each query and their corresponding results. The multi-level memory
first addresses queries, followed by results and finally each key-value pair
within a result. We conduct detailed experiments on three publicly available
task oriented dialog data sets and we find that our method conclusively
outperforms current state-of-the-art models. We report a 15-25% increase in
both entity F1 and BLEU scores.Comment: Accepted as full paper at NAACL 201
A survey of agent-oriented methodologies
This article introduces the current agent-oriented methodologies. It discusses what approaches have been followed (mainly extending existing object oriented and knowledge engineering methodologies), the suitability of these approaches for agent modelling, and some conclusions drawn from the survey
Organisational Abstractions for the Analysis and Design of Multi-Agent Systems
The architecture of a multi-agent system can naturally be viewed as a computational organisation. For this reason, we believe organisational abstractions should play a central role in the analysis and design of such systems. To this end, the concepts of agent roles and role models are increasingly being used to specify and design multi-agent systems. However, this is not the full picture. In this paper we introduce three additional organisational concepts - organisational rules, organisational structures, and organisational patterns - that we believe are necessary for the complete specification of computational organisations. We view the introduction of these concepts as a step towards a comprehensive methodology for agent-oriented systems
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