Patrick Krümpelmann

Belief base change operations for answer set programming

We present a principled approach to the problem of belief revision in (non-monotonic) logic programming under the answer set semantics. Unlike previous approaches we use a belief base approach. Belief bases are sets of sentences that are, in contrast to belief sets, not deductively closed. We show that many of the classic base revision postulates are applicable to the logic programming case. We discuss further postulates for logic program revision and show that many of them follow from classical base revision postulates. For those postulates that do not follow from base revision postulates we propose new postulates that may also be justified from the base revision perspective. Moreover we develop a new construction for prioritized multiple base revision based on a consolidation operation via remainder sets. This construction is applicable in both the classical propositional and the logic programming cases. We connect postulates and construction by proving a representation theorem showing that the construction is exactly characterized by the proposed set of postulates.

Selective revision by deductive argumentation

The success postulate of classic belief revision theory demands that after revising some beliefs with by information the new information is believed. However, this form of prioritized belief revision is not apt under many circumstances. Research in non-prioritized belief revision investigates forms of belief revision where success is not a desirable property. Herein, selective revision uses a two step approach, first applying a transformation function to decide if and which part of the new information shall be accepted, and second, incorporating the result using a prioritized revision operator. In this paper, we implement a transformation function by employing deductive argumentation to assess the value of new information. Hereby we obtain a non-prioritized revision operator that only accepts new information if believing in the information is justifiable with respect to the beliefs. By making use of previous results on selective revision we prove that our revision operator satisfies several desirable properties. We illustrate the use of the revision operator by means of examples and compare it with related work.

Dependency semantics for sequences of extended logic programs

In this work, we present a framework based on the notion of dependencies. We use this framework to define semantics for inconsistent belief bases in a modular way to define general tools for handling inconsistency. We consider belief bases represented by non-monotonic formalisms, and in particular use extended logic programs and belief bases represented by sequences of these. We show the presented frameworks appliance with the answer set semantics for consistent belief bases. Moreover, we define various instantiations of the framework and show relations to other approaches. We present ways to improve the resulting semantics by means of changes to modules of the framework that lead to the definition of improved approaches to conflict handling in logic programming-based knowledge bases.

Forwarding Credible Information in Multi-agent Systems

In this work we extend the communication abilities of agents in multi-agent systems by enabling them to reason about the credibility of information to be shared with other agents. We propose a framework in which agents exchange sentences of a logical language enriched by meta-information. We discuss several possible approaches and present an advanced approach overcoming previously shown problems. For this, we make use of a calculation method for the plausibility of information known from approaches to belief dynamics in multi-agent systems. Moreover, we present how this can be implemented in a multi-agent system.

A constructive approach to independent and evidence retaining belief revision by general information sets

Recent years have seen a lot of work towards extending the established AGM belief revision theory with respect to iterating revision, preserving conditional beliefs, and handling sets of propositions as new information. In particular, novel postulates like independence and evidence retainment have been brought forth as new standards for revising epistemic states by (sets of) propositional information. In this paper, we propose a constructive approach for revising epistemic states by sets of (propositional and conditional) beliefs that combines ideas from nonmonotonic reasoning with conditional belief revision. We also propose a novel principle called enforcement that covers both independence and evidence retainment, and we show our revision operator to comply with major postulates from the literature. Moreover, we point out the relevance of our approach for default reasoning.

Motivating agents in unreliable environments: a computational model

The development of formalmodels for rational agents ismainly driven by the well-established BDI approach which divides an agents mental state into beliefs, desires, and intentions. In this paper, we argue that motivation as well has to be taken into account in order to allow for a flexible and proactive behavior of intelligent agents in unreliable environments. In our approach, motives take the role of describing an agents personality and are the driving force for creating desires and abandoning previously selected goals. We investigate the relationships between motives and their associated desires as well as the impact brought about by the uncertainty and unreliability of the environment.

Reasoning on Secrecy Constraints under Uncertainty to Classify Possible Actions

Within a multiagent system, we focus on an intelligent agent

Construction of Explanation Graphs from Extended Dependency Graphs for Answer Set Programs

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On influence and contractions in defeasible logic programming

maintaining a view on the world and interacting with another agent

Angerona - A Flexible Multiagent Framework for Knowledge-Based Agents

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