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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
Logic-Based Specification Languages for Intelligent Software Agents
The research field of Agent-Oriented Software Engineering (AOSE) aims to find
abstractions, languages, methodologies and toolkits for modeling, verifying,
validating and prototyping complex applications conceptualized as Multiagent
Systems (MASs). A very lively research sub-field studies how formal methods can
be used for AOSE. This paper presents a detailed survey of six logic-based
executable agent specification languages that have been chosen for their
potential to be integrated in our ARPEGGIO project, an open framework for
specifying and prototyping a MAS. The six languages are ConGoLog, Agent-0, the
IMPACT agent programming language, DyLog, Concurrent METATEM and Ehhf. For each
executable language, the logic foundations are described and an example of use
is shown. A comparison of the six languages and a survey of similar approaches
complete the paper, together with considerations of the advantages of using
logic-based languages in MAS modeling and prototyping.Comment: 67 pages, 1 table, 1 figure. Accepted for publication by the Journal
"Theory and Practice of Logic Programming", volume 4, Maurice Bruynooghe
Editor-in-Chie
State-of-the-art on evolution and reactivity
This report starts by, in Chapter 1, outlining aspects of querying and updating resources on
the Web and on the Semantic Web, including the development of query and update languages
to be carried out within the Rewerse project.
From this outline, it becomes clear that several existing research areas and topics are of
interest for this work in Rewerse. In the remainder of this report we further present state of
the art surveys in a selection of such areas and topics. More precisely: in Chapter 2 we give
an overview of logics for reasoning about state change and updates; Chapter 3 is devoted to briefly describing existing update languages for the Web, and also for updating logic programs;
in Chapter 4 event-condition-action rules, both in the context of active database systems and
in the context of semistructured data, are surveyed; in Chapter 5 we give an overview of some relevant rule-based agents frameworks
Inductive learning spatial attention
This paper investigates the automatic induction of spatial attention
from the visual observation of objects manipulated
on a table top. In this work, space is represented in terms of
a novel observer-object relative reference system, named Local
Cardinal System, defined upon the local neighbourhood
of objects on the table. We present results of applying the
proposed methodology on five distinct scenarios involving
the construction of spatial patterns of coloured blocks
Modelling and Explaining Legal Case-based Reasoners through Classifiers
This paper brings together factor-based models of case-based reasoning
(CBR) and the logical specification of classifiers. Horty [8] has developed the
factor-based models of precedent into a theory of precedential constraint. In this
paper we combine binary-input classifier logic (BCL) to classifiers and their explanations
given by Liu & Lorini [13,14] with Horty’s account of factor-based CBR,
since both a classifier and CBR map sets of features to decisions or classifications.
We reformulate case bases in the language of BCL, and give several representation
results. Furthermore, we show how notions of CBR can be analyzed by notions of
classifier explanation
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
Forgetting complex propositions
This paper uses possible-world semantics to model the changes that may occur
in an agent's knowledge as she loses information. This builds on previous work
in which the agent may forget the truth-value of an atomic proposition, to a
more general case where she may forget the truth-value of a propositional
formula. The generalization poses some challenges, since in order to forget
whether a complex proposition is the case, the agent must also lose
information about the propositional atoms that appear in it, and there is no
unambiguous way to go about this.
We resolve this situation by considering expressions of the form
, which quantify over all possible (but
minimal) ways of forgetting whether . Propositional atoms are modified
non-deterministically, although uniformly, in all possible worlds. We then
represent this within action model logic in order to give a sound and complete
axiomatization for a logic with knowledge and forgetting. Finally, some
variants are discussed, such as when an agent forgets (rather than
forgets whether ) and when the modification of atomic facts is done
non-uniformly throughout the model
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