Complexity of Prioritized Default Logics

In default reasoning, usually not all possible ways of resolving conflicts between default rules are acceptable. Criteria expressing acceptable ways of resolving the conflicts may be hardwired in the inference mechanism, for example specificity in inheritance reasoning can be handled this way, or they may be given abstractly as an ordering on the default rules. In this article we investigate formalizations of the latter approach in Reiter's default logic. Our goal is to analyze and compare the computational properties of three such formalizations in terms of their computational complexity: the prioritized default logics of Baader and Hollunder, and Brewka, and a prioritized default logic that is based on lexicographic comparison. The analysis locates the propositional variants of these logics on the second and third levels of the polynomial hierarchy, and identifies the boundary between tractable and intractable inference for restricted classes of prioritized default theories

An Incremental Model of Anaphora and Reference Resolution Based on Resource Situations

Notwithstanding conclusive psychological and corpus evidence that at least some aspects of anaphoric and referential interpretation take place incrementally, and the existence of some computational models of incremental reference resolution, many aspects of the linguistics of incremental reference interpretation still have to be better understood. We propose a model of incremental reference interpretation based on Loebner’s theory of definiteness and on the theory of anaphoric accessibility via resource situations developed in Situation Semantics, and show how this model can account for a variety of psychological results about incremental reference interpretation

Answer Sets for Consistent Query Answering in Inconsistent Databases

A relational database is inconsistent if it does not satisfy a given set of integrity constraints. Nevertheless, it is likely that most of the data in it is consistent with the constraints. In this paper we apply logic programming based on answer sets to the problem of retrieving consistent information from a possibly inconsistent database. Since consistent information persists from the original database to every of its minimal repairs, the approach is based on a specification of database repairs using disjunctive logic programs with exceptions, whose answer set semantics can be represented and computed by systems that implement stable model semantics. These programs allow us to declare persistence by defaults and repairing changes by exceptions. We concentrate mainly on logic programs for binary integrity constraints, among which we find most of the integrity constraints found in practice.Comment: 34 page

Borhan: A Novel System for Prioritized Default Logic

Prioritized Default Logic presents an optimal solution for addressing real-world problems characterized by incomplete information and the need to establish preferences among diverse scenarios. Although it has reached great success in the theoretical aspect, its practical implementation has received less attention. In this article, we introduce Borhan, a system designed and created for prioritized default logic reasoning. To create an effective system, we have refined existing default logic definitions, including the extension concept, and introduced novel concepts. In addition to its theoretical merits, Borhan proves its practical utility by efficiently addressing a range of prioritized default logic problems. In addition, one of the advantages of our system is its ability to both store and report the explanation path for any inferred triple, enhancing transparency and interpretability. Borhan is offered as an open-source system, implemented in Python, and even offers a simplified Java version as a plugin for the Protege ontology editor. Borhan thus represents a significant step forward in bridging the gap between the theoretical foundations of default logic and its real-world applications

Monitoring Computer Systems: An Intelligent Approach

Monitoring modern computer systems is increasingly difficult due to their peculiar characteristics. To cope with this situation, the dissertation develops an approach to intelligent monitoring. The resulting model consists of three major designs: representing targets, controlling data collection, and autonomously refining monitoring performance. The model explores a more declarative object-oriented model by introducing virtual objects to dynamically compose abstract representations, while it treats conventional hard-wired hierarchies and predefined object classes as primitive structures. Taking the representational framework as a reasoning bed, the design for controlling mechanisms adopts default reasoning backed up with ordered constraints, so that the amount of data collected, levels of details, semantics, and resolution of observation can be appropriately controlled. The refining mechanisms classify invoked knowledge and update the classified knowledge in terms of the feedback from monitoring. The approach is designed first and then formally specified. Applications of the resulting model are examined and an operational prototype is implemented. Thus the dissertation establishes a basis for an approach to intelligent monitoring, one which would be equipped to deal effectively with the difficulties that arise in monitoring modern computer systems

Prioritized Conditional Imperatives:Problems and a New Proposal

The sentences of deontic logic may be understood as describing what an agent ought to do when faced with a given set of norms. If these norms come into conflict, the best the agent can be expected to do is to follow a maximal subset of the norms. Intuitively, a priority ordering of the norms can be helpful in determining the relevant sets and resolve conflicts, but a formal resolution mechanism has been difficult to provide. In particular, reasoning about prioritized conditional imperatives is overshadowed by problems such as the `order puzzle\u27 that are not satisfactorily resolved by existing approaches. The paper provides a new proposal as to how these problems may be overcome

A RULE-BASED APPROACH TO ANIMATING MULTI-AGENT ENVIRONMENTS

This dissertation describes ESCAPE (Expert Systems in Computer Animation Production Environments), a multi-agent animation system for building domain-oriented, rule-based visual programming environments. Much recent work in computer graphics has been concerned with producing behavioural animations of artificial life-forms mainly based on algorithmic approaches. This research indicates how, by adding an inference engine and rules that describe such behaviour, traditional computer animation environments can be enhanced. The comparison between using algorithmic approaches and using a rule-based approach for representing multi-agent worlds is not based upon their respective claims to completeness, but rather on the ease with which end users may express their knowledge and control their animations with a minimum of technical knowledge. An environment for the design of computer animations incorporating an expert system approach is described. In addition to direct manipulation of objects on the screen, the environment allows users to describe behavioural rules based upon both the physical and non-physical attributes of objects. These rules can be interpreted to suggest the transition from stage to stage or to automatically produce a longer animation. The output from the system can be integrated into a commercially available 3D modelling and rendering package. Experience indicates that a hybrid environment, mixing algorithmic and rule-based approaches, would be very promising and offer benefits in application areas such as creating realistic background scenes and modelling human beings or animals either singly or in groups. A prototype evaluation system and three different domains are described and illustrated with preliminary animated images