Decrement Operators in Belief Change

While research on iterated revision is predominant in the field of iterated belief change, the class of iterated contraction operators received more attention in recent years. In this article, we examine a non-prioritized generalisation of iterated contraction. In particular, the class of weak decrement operators is introduced, which are operators that by multiple steps achieve the same as a contraction. Inspired by Darwiche and Pearl's work on iterated revision the subclass of decrement operators is defined. For both, decrement and weak decrement operators, postulates are presented and for each of them a representation theorem in the framework of total preorders is given. Furthermore, we present two sub-types of decrement operators

Ontology-based specific and exhaustive user profiles for constraint information fusion for multi-agents

Intelligent agents are an advanced technology utilized in Web Intelligence. When searching information from a distributed Web environment, information is retrieved by multi-agents on the client site and fused on the broker site. The current information fusion techniques rely on cooperation of agents to provide statistics. Such techniques are computationally expensive and unrealistic in the real world. In this paper, we introduce a model that uses a world ontology constructed from the Dewey Decimal Classification to acquire user profiles. By search using specific and exhaustive user profiles, information fusion techniques no longer rely on the statistics provided by agents. The model has been successfully evaluated using the large INEX data set simulating the distributed Web environment

Speeding up Lazy-Grounding Answer Set Solving

The grounding bottleneck is an important open issue in Answer Set Programming. Lazy grounding addresses it by interleaving grounding and search. The performance of current lazy-grounding solvers is not yet comparable to that of ground-and-solve systems, however. The aim of this thesis is to extend prior work on lazy grounding by novel heuristics and other techniques like non-ground conflict learning in order to speed up solving. Parts of expected results will be beneficial for ground-and-solve systems as well

Practical undoability checking via contingent planning

We consider a general concept of undoability, asking whether a given action can always be undone, no matter which state it is applied to. This generalizes previous concepts of invertibility, and is relevant for search as well as applications. Naïve undoability checking requires to enumerate all states an action is applicable to. Extending and operationalizing prior work in this direction, we introduce a compilation into contingent planning, replacing such enumeration by standard techniques handling large belief states. We furthermore introduce compilations for checking whether one can always get back to an at-least-as-good state, as well as for determining partial undoability, i. e., undoability on a subset of states an action is applicable to. Our experiments on IPC benchmarks and in a cloud management application show that contingent planners are often effective at solving this kind of problem, hence providing a practical means for undoability checking

Penalization Framework For Autonomous Agents Using Answer Set Programming

This paper presents a framework for enforcing penalties on intelligent agents that do not comply with authorization or obligation policies in a changing environment. A framework is proposed to represent and reason about penalties in plans, and an algorithm is proposed to penalize an agent's actions based on their level of compliance with respect to authorization and obligation policies. Being aware of penalties an agent can choose a plan with a minimal total penalty, unless there is an emergency goal like saving a human's life. The paper concludes that this framework can reprimand insubordinate agents.Comment: In Proceedings ICLP 2023, arXiv:2308.1489

An approach to handling inconsistent ontology definitions based on the translation of description logics into defeasible logic programming

The Semantic Web is a future vision of the web where stored information has exact meaning, thus enabling computers to understand and reason on the basis of such information. Assigning semantics to web resources is addressed by means of ontology definitions which are meant to be written in an ontology description language such as OWL-DL that is based on so-called Description Logics (DL). Although ontology definitions expressed in DL can be processed with existing DL reasoners, such DL reasoners are incapable of dealing with inconsistent ontology definitions. Previous research has determined that a subset of DL can be effectively translated into an equivalent subset of logic programming. We propose a method for dealing with inconsistent ontology definitions in the Semantic Web. Our proposal involves mapping DL ontologies into equivalent DeLP programs. That is, given an OWL-DL ontology OOwl, an equivalent DL ontology ODL can be obtained. Provided ODL satisfies certain restrictions, it can be translated into an equivalent DeLP program ODeLP . Therefore, given a query Q w.r.t. OOwl, a dialectical process will be performed to determine if Q is warranted w.r.t. ODeLP .VII Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI