Belief Revision in Multi-Agent Systems

The ability to respond sensibly to changing and conflicting beliefs is an integral part of intelligent agency. To this end, we outline the design and implementation of a Distributed Assumption-based Truth Maintenance System (DATMS) appropriate for controlling cooperative problem solving in a dynamic real world multi-agent community. Our DATMS works on the principle of local coherence which means that different agents can have different perspectives on the same fact provided that these stances are appropriately justified. The belief revision algorithm is presented, the meta-level code needed to ensure that all system-wide queries can be uniquely answered is described, and the DATMS’ implementation in a general purpose multi-agent shell is discussed

An comparative analysis of different models of belief revision using information from multiple sources

In this work we analyze the problem of knowledge representation in a collaborative multi-agent system where agents can obtain new information from others through communication. Namely, we analyze several approaches of belief revision in multi-agent systems. We will describe different research lines in this topic and we will focus on Belief Revision using Information from Multiple Sources. For this, we are going to accomplish a comparative analysis of different models of belief revision that use information from multiple sources.Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Generalizing AGM to a multi-agent setting

International audienceWe generalize AGM belief revision theory to the multi-agent case. To do so, we first generalize the semantics of the single-agent case, based on the notion of interpretation, to the multi-agent case. Then we show that, thanks to the shape of our new semantics, all the results of the AGM framework transfer. Afterwards we investigate some postulates that are specific to our multi-agent setting. Finally, we give an example of revision operator that fulfills one of these new postulates and give an example of revision on a concrete example

Internal models and private multi-agent belief revision

International audienceWe generalize AGM belief revision theory to the multi-agent case. To do so, we first generalize the semantics of the single- agent case, based on the notion of interpretation, to the multi-agent case. Then we show that, thanks to the shape of our new semantics, all the results of the AGM framework transfer. Afterwards we investigate some postulates that are specific to our multi-agent setting

A fixed-point property of logic-based bargaining solution

Abstract. This paper presents a logic-based bargaining solution based on Zhang and Zhang’s framework. It is shown that if the demand sets of players are logically closed, the solution satisfies a fixed-point property, which says that the outcome of bargaining is the result of mutual belief revision. The result is interesting not only because it presents a desirable logical property of bargaining solution but also establishes a link between bargaining theory and multi-agent belief revision.

An comparative analysis of different models of belief revision using information from multiple sources

In this work we analyze the problem of knowledge representation in a collaborative multi-agent system where agents can obtain new information from others through communication. Namely, we analyze several approaches of belief revision in multi-agent systems. We will describe different research lines in this topic and we will focus on Belief Revision using Information from Multiple Sources. For this, we are going to accomplish a comparative analysis of different models of belief revision that use information from multiple sources.Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Belief Change in Reasoning Agents: Axiomatizations, Semantics and Computations

The capability of changing beliefs upon new information in a rational and efficient way is crucial for an intelligent agent. Belief change therefore is one of the central research fields in Artificial Intelligence (AI) for over two decades. In the AI literature, two different kinds of belief change operations have been intensively investigated: belief update, which deal with situations where the new information describes changes of the world; and belief revision, which assumes the world is static. As another important research area in AI, reasoning about actions mainly studies the problem of representing and reasoning about effects of actions. These two research fields are closely related and apply a common underlying principle, that is, an agent should change its beliefs (knowledge) as little as possible whenever an adjustment is necessary. This lays down the possibility of reusing the ideas and results of one field in the other, and vice verse. This thesis aims to develop a general framework and devise computational models that are applicable in reasoning about actions. Firstly, I shall propose a new framework for iterated belief revision by introducing a new postulate to the existing AGM/DP postulates, which provides general criteria for the design of iterated revision operators. Secondly, based on the new framework, a concrete iterated revision operator is devised. The semantic model of the operator gives nice intuitions and helps to show its satisfiability of desirable postulates. I also show that the computational model of the operator is almost optimal in time and space-complexity. In order to deal with the belief change problem in multi-agent systems, I introduce a concept of mutual belief revision which is concerned with information exchange among agents. A concrete mutual revision operator is devised by generalizing the iterated revision operator. Likewise, a semantic model is used to show the intuition and many nice properties of the mutual revision operator, and the complexity of its computational model is formally analyzed. Finally, I present a belief update operator, which takes into account two important problems of reasoning about action, i.e., disjunctive updates and domain constraints. Again, the updated operator is presented with both a semantic model and a computational model

Improving Assumption based Distributed Belief Revision

Belief revision is a critical issue in real world DAI applications. A Multi-Agent System not only has to cope with the intrinsic incompleteness and the constant change of the available knowledge (as in the case of its stand alone counterparts), but also has to deal with possible conflicts between the agents’ perspectives. Each semi-autonomous agent, designed as a combination of a problem solver – assumption based truth maintenance system (ATMS), was enriched with improved capabilities: a distributed context management facility allowing the user to dynamically focus on the more pertinent contexts, and a distributed belief revision algorithm with two levels of consistency. This work contributions include: (i) a concise representation of the shared external facts; (ii) a simple and innovative methodology to achieve distributed context management; and (iii) a reduced inter-agent data exchange format. The different levels of consistency adopted were based on the relevance of the data under consideration: higher relevance data (detected inconsistencies) was granted global consistency while less relevant data (system facts) was assigned local consistency. These abilities are fully supported by the ATMS standard functionalities

Non prioritized answer set revision

In this paper, we build on previous work on Belief Revision operators based on the use of logic programming with Answer Set semantics as a representation language. We present a set of postulates for Answer Set Revision with respect to a set of sentences and with respect to explanations. We focus on the non-prioritized revision operator with respect to explanations, or arguments, which is intended to model situations in which agents revise their knowledge as a result of dialogues with other agents in a multi-agent setting.Eje: V - Workshop de agentes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI