660 research outputs found
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
Many-valued logics. A mathematical and computational introduction.
2nd edition. Many-valued logics are those logics that have more than the two classical truth values, to wit, true and false; in fact, they can have from three to infinitely many truth values. This property, together with truth-functionality, provides a powerful formalism to reason in settings where classical logicāas well as other non-classical logicsāis of no avail. Indeed, originally motivated by philosophical concerns, these logics soon proved relevant for a plethora of applications ranging from switching theory to cognitive modeling, and they are today in more demand than ever, due to the realization that inconsistency and vagueness in knowledge bases and information processes are not only inevitable and acceptable, but also perhaps welcome.
The main modern applications of (any) logic are to be found in the digital computer, and we thus require the practical knowledge how to computerizeāwhich also means automateādecisions (i.e. reasoning) in many-valued logics. This, in turn, necessitates a mathematical foundation for these logics. This book provides both these mathematical foundation and practical knowledge in a rigorous, yet accessible, text, while at the same time situating these logics in the context of the satisfiability problem (SAT) and automated deduction.
The main text is complemented with a large selection of exercises, a plus for the reader wishing to not only learn about, but also do something with, many-valued logics
New Models for Expert System Design
This thesis presents new work on the analysis of human lung sound. Experimental studies investigated the relationship between the condition of the lungs and the power spectrum of lung sound detected at the chest wall. The conclusion drawn from two clinical studies was that the median frequency of the lung sound power spectrum increases with a decrease in airway calibre. The technique for the analysis of lung sound presented in this thesis is a non-invasive method which may be capable of assessing differences in airway calibre between different lobes of the lung. An expert system for the analysis of lung sound data and pulmonary function data was designed. The expert knowledge was expressed in a belief logic, a system of logic which is more expressive than first order logic. New automated theorem proving methods were developed for the belief logic. The new methods were implemented to form the 'inference engine' of the expert system. The new expert system compared favourably with systems which perform a similar task. The use of belief logic allows introspective reasoning to be carried out. Plausible reasoning, a type of introspective reasoning which allows conclusions to be drawn when the database is incomplete, was proposed and tested. The author concludes that the use of a belief logic in expert system design has significant advantages over conventional approaches. The experimental results of the lung sound research were incorporated into the expert system rule base: the medical and expert system research were complementary
Distributed Reasoning in a Peer-to-Peer Setting: Application to the Semantic Web
In a peer-to-peer inference system, each peer can reason locally but can also
solicit some of its acquaintances, which are peers sharing part of its
vocabulary. In this paper, we consider peer-to-peer inference systems in which
the local theory of each peer is a set of propositional clauses defined upon a
local vocabulary. An important characteristic of peer-to-peer inference systems
is that the global theory (the union of all peer theories) is not known (as
opposed to partition-based reasoning systems). The main contribution of this
paper is to provide the first consequence finding algorithm in a peer-to-peer
setting: DeCA. It is anytime and computes consequences gradually from the
solicited peer to peers that are more and more distant. We exhibit a sufficient
condition on the acquaintance graph of the peer-to-peer inference system for
guaranteeing the completeness of this algorithm. Another important contribution
is to apply this general distributed reasoning setting to the setting of the
Semantic Web through the Somewhere semantic peer-to-peer data management
system. The last contribution of this paper is to provide an experimental
analysis of the scalability of the peer-to-peer infrastructure that we propose,
on large networks of 1000 peers
Proceedings of the 11th European Agent Systems Summer School Student Session
This volume contains the papers presented at the Student Session of the 11th European Agent Systems Summer School (EASSS) held on 2nd of September 2009 at Educatorio della Providenza, Turin, Italy. The Student Session, organised by students, is designed to encourage student interaction and feedback from the tutors. By providing the students with a conference-like setup, both in the presentation and in the review process, students have the opportunity to prepare their own submission, go through the selection process and present their work to each other and their interests to their fellow students as well as internationally leading experts in the agent field, both from the theoretical and the practical sector. Table of Contents: Andrew Koster, Jordi Sabater Mir and Marco Schorlemmer, Towards an inductive algorithm for learning trust alignment . . . 5; Angel Rolando Medellin, Katie Atkinson and Peter McBurney, A Preliminary Proposal for Model Checking Command Dialogues. . . 12; Declan Mungovan, Enda Howley and Jim Duggan, Norm Convergence in Populations of Dynamically Interacting Agents . . . 19; Akın GĆ¼nay, Argumentation on Bayesian Networks for Distributed Decision Making . . 25; Michael Burkhardt, Marco Luetzenberger and Nils Masuch, Towards Toolipse 2: Tool Support for the JIAC V Agent Framework . . . 30; Joseph El Gemayel, The Tenacity of Social Actors . . . 33; Cristian Gratie, The Impact of Routing on Traffic Congestion . . . 36; Andrei-Horia Mogos and Monica Cristina Voinescu, A Rule-Based Psychologist Agent for Improving the Performances of a Sportsman . . . 39; --Autonomer Agent,Agent,KĆ¼nstliche Intelligenz
Modelling default and likelihood reasoning as probabilistic
A probabilistic analysis of plausible reasoning about defaults and about likelihood is presented. 'Likely' and 'by default' are in fact treated as duals in the same sense as 'possibility' and 'necessity'. To model these four forms probabilistically, a logic QDP and its quantitative counterpart DP are derived that allow qualitative and corresponding quantitative reasoning. Consistency and consequence results for subsets of the logics are given that require at most a quadratic number of satisfiability tests in the underlying propositional logic. The quantitative logic shows how to track the propagation error inherent in these reasoning forms. The methodology and sound framework of the system highlights their approximate nature, the dualities, and the need for complementary reasoning about relevance
Default reasoning and neural networks
In this dissertation a formalisation of nonmonotonic reasoning, namely Default logic, is discussed. A proof theory for default logic and a variant of Default logic - Prioritised Default logic - is presented. We also pursue an investigation into the relationship between default reasoning and making inferences in a neural network. The inference problem shifts from the logical problem in Default logic to the optimisation problem in neural networks, in which maximum consistency is aimed at The inference is realised as an adaptation process that identifies and resolves conflicts between existing knowledge about the relevant world and external information. Knowledge and
data are transformed into constraint equations and the nodes in the network represent propositions and constraint equations. The violation of constraints is formulated in terms of an energy function. The Hopfield network is shown to be suitable for modelling optimisation problems and default reasoning.Computer ScienceM.Sc. (Computer Science
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