110,637 research outputs found
A Polynomial Translation of Logic Programs with Nested Expressions into Disjunctive Logic Programs: Preliminary Report
Nested logic programs have recently been introduced in order to allow for
arbitrarily nested formulas in the heads and the bodies of logic program rules
under the answer sets semantics. Nested expressions can be formed using
conjunction, disjunction, as well as the negation as failure operator in an
unrestricted fashion. This provides a very flexible and compact framework for
knowledge representation and reasoning. Previous results show that nested logic
programs can be transformed into standard (unnested) disjunctive logic programs
in an elementary way, applying the negation as failure operator to body
literals only. This is of great practical relevance since it allows us to
evaluate nested logic programs by means of off-the-shelf disjunctive logic
programming systems, like DLV. However, it turns out that this straightforward
transformation results in an exponential blow-up in the worst-case, despite the
fact that complexity results indicate that there is a polynomial translation
among both formalisms. In this paper, we take up this challenge and provide a
polynomial translation of logic programs with nested expressions into
disjunctive logic programs. Moreover, we show that this translation is modular
and (strongly) faithful. We have implemented both the straightforward as well
as our advanced transformation; the resulting compiler serves as a front-end to
DLV and is publicly available on the Web.Comment: 10 pages; published in Proceedings of the 9th International Workshop
on Non-Monotonic Reasonin
Utility Design for Distributed Resource Allocation -- Part I: Characterizing and Optimizing the Exact Price of Anarchy
Game theory has emerged as a fruitful paradigm for the design of networked
multiagent systems. A fundamental component of this approach is the design of
agents' utility functions so that their self-interested maximization results in
a desirable collective behavior. In this work we focus on a well-studied class
of distributed resource allocation problems where each agent is requested to
select a subset of resources with the goal of optimizing a given system-level
objective. Our core contribution is the development of a novel framework to
tightly characterize the worst case performance of any resulting Nash
equilibrium (price of anarchy) as a function of the chosen agents' utility
functions. Leveraging this result, we identify how to design such utilities so
as to optimize the price of anarchy through a tractable linear program. This
provides us with a priori performance certificates applicable to any existing
learning algorithm capable of driving the system to an equilibrium. Part II of
this work specializes these results to submodular and supermodular objectives,
discusses the complexity of computing Nash equilibria, and provides multiple
illustrations of the theoretical findings.Comment: 15 pages, 5 figure
(WP 2019-01) Stratification Economics as an Economics of Exclusion
Stratification Economics (SE) is an emergent sub-field in economics, but its JEL classification misrepresents its content and its relationship to the whole of economics. This paper first develops a more accurate characterization of SE by identifying its differences with Mainstream Economics (ME), its commonalities with economics in a broad sense, and how the combination of these differences and commonalities define it as a distinct research program. It then applies this definition to an economic goods taxonomy that makes a distinction between local public goods and common pool goods to interpret SE’S distinct research program as an economics of exclusion. The paper closes with a discussion of how SE might explain socioeconomic change in social group identity terms
Computer Modeling of Personal Autonomy and Legal Equilibrium
Empirical studies of personal autonomy as state and status of individual
freedom, security, and capacity to control own life, particularly by
independent legal reasoning, are need dependable models and methods of precise
computation. Three simple models of personal autonomy are proposed. The linear
model of personal autonomy displays a relation between freedom as an amount of
agent's action and responsibility as an amount of legal reaction and shows
legal equilibrium, the balance of rights and duties needed for sustainable
development of any community. The model algorithm of judge personal autonomy
shows that judicial decision making can be partly automated, like other human
jobs. Model machine learning of autonomous lawyer robot under operating system
constitution illustrates the idea of robot rights. Robots, i.e. material and
virtual mechanisms serving the people, deserve some legal guarantees of their
rights such as robot rights to exist, proper function and be protected by the
law. Robots, actually, are protected as any human property by the wide scope of
laws, starting with Article 17 of Universal Declaration of Human Rights, but
the current level of human trust in autonomous devices and their role in
contemporary society needs stronger legislation to guarantee the robot rights.Comment: 8 pages, 6 figures, presented at Computer Science On-line Conference
201
Asynchronous Multi-Context Systems
In this work, we present asynchronous multi-context systems (aMCSs), which
provide a framework for loosely coupling different knowledge representation
formalisms that allows for online reasoning in a dynamic environment. Systems
of this kind may interact with the outside world via input and output streams
and may therefore react to a continuous flow of external information. In
contrast to recent proposals, contexts in an aMCS communicate with each other
in an asynchronous way which fits the needs of many application domains and is
beneficial for scalability. The federal semantics of aMCSs renders our
framework an integration approach rather than a knowledge representation
formalism itself. We illustrate the introduced concepts by means of an example
scenario dealing with rescue services. In addition, we compare aMCSs to
reactive multi-context systems and describe how to simulate the latter with our
novel approach.Comment: International Workshop on Reactive Concepts in Knowledge
Representation (ReactKnow 2014), co-located with the 21st European Conference
on Artificial Intelligence (ECAI 2014). Proceedings of the International
Workshop on Reactive Concepts in Knowledge Representation (ReactKnow 2014),
pages 31-37, technical report, ISSN 1430-3701, Leipzig University, 2014.
http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-15056
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