Resource-Bounded Belief Revision and Contraction

Alechina, Natasha; Jago, Mark; Logan, Brian

doi:10.1007/11691792_9

Cited by 17 publications

(40 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This makes a belief revision strategy necessary: the agent needs to have a way of removing enough facts from its knowledge base to make sure that a contradiction is no longer derivable. This paper extends work reported by Alechina et al (2006b;2006a). In (Alechina et al, 2006b), a belief contraction algorithm for rule-based agents was described which runs in time linear in the size of the agent's knowledge base in the propositional case.…”

Section: Introductionsupporting

confidence: 57%

“…This paper extends work reported by Alechina et al (2006b;2006a). In (Alechina et al, 2006b), a belief contraction algorithm for rule-based agents was described which runs in time linear in the size of the agent's knowledge base in the propositional case. It was also shown that the operation defined by the algorithm satisfies all but one of the basic Alchourrón, Gärdenfors and Makinson (AGM) postulates.…”

Section: Introductionsupporting

confidence: 57%

“…The last step in the contraction algorithm (removing a belief) is executed at most n times. Note that in the propositional case, when the number of rules equals the number of rule instances, the algorithm runs in time linear in the size of the agent's belief set (Alechina et al, 2006b).…”

Section: Complexitymentioning

confidence: 99%

“…Alechina et al (2006a) showed how this algorithm can be incorporated in the AgentSpeak agent programming language. In this paper, we give extensional definitions of the contraction and revision operations from (Alechina et al, 2006b) and prove representation theorems for each operation.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Preference-based belief revision for rule-based agents

2008

Self Cite

View full text Add to dashboard Cite

Abstract. Agents which perform inferences on the basis of unreliable information need an ability to revise their beliefs if they discover an inconsistency. Such a belief revision algorithm ideally should be rational, should respect any preference ordering over the agent's beliefs (removing less preferred beliefs where possible) and should be fast. However, while standard approaches to rational belief revision for classical reasoners allow preferences to be taken into account, they typically have quite high complexity. In this paper, we consider belief revision for agents which reason in a simpler logic than full first-order logic, namely rule-based reasoners. We show that it is possible to define a contraction operation for rule-based reasoners, which we call McAllester contraction, which satisfies all the basic Alchourrón, Gärdenfors and Makinson (AGM) postulates for contraction (apart from the recovery postulate) and at the same time can be computed in polynomial time. We prove a representation theorem for McAllester contraction with respect to the basic AGM postulates (minus recovery), and two additional postulates. We then show that our contraction operation removes a set of beliefs which is least preferred, with respect to a natural interpretation of preference. Finally, we show how McAllester contraction can be used to define a revision operation which is also polynomial time, and prove a representation theorem for the revision operation.

show abstract

Section: Introductionsupporting

confidence: 57%

Section: Introductionsupporting

confidence: 57%

Section: Complexitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Preference-based belief revision for rule-based agents

2008

Self Cite

View full text Add to dashboard Cite

show abstract

“…For this reason, belief revision for resource-bounded agents has become an increasingly popular research area, leading to implementable logics for agent [6,5]. On the other hand, logic programming and extensions have provided efficient techniques to handle many types of nonmonotonic and hypothetical reasoning (we elaborate on this later).…”

Section: Logic-based Agent Description [Formal Specification]mentioning

confidence: 99%

Computational Logics and Agents: A Road Map of Current Technologies and Future Trends

Fisher

Bordini

Hirsch

et al. 2007

Computational Intelligence

View full text Add to dashboard Cite

The concept of an agent is increasingly used in contemporary software applications, particularly those involving the Internet, autonomous systems, or cooperation. However, with dependability and safety in mind, it is vital that the mechanisms for representing and implementing agents are clear and consistent. Hence there has been a strong research effort directed at using formal logic as the basis for agent descriptions and agent implementation. Such a logical basis not only presents the clarity and consistency required but also allows for important techniques such as logical verification to be applied.We present a roadmap of research into the use of computational logic in agent-based systems and survey much of the recent work in these areas. Even though, with such a rapidly changing field, it is impossible to cover every development, we aim to give the reader sufficient background to understand the current research problems and potential future developments in this maturing area.

show abstract