Maurice Pagnucco

Iterated belief change in the situation calculus

John McCarthys situation calculus has left an enduring mark on artificial intelligence research. This simple yet elegant formalism for modelling and reasoning about dynamic systems is still in common use more than forty years since it was first proposed. The ability to reason about action and change has long been considered a necessary component for any intelligent system. The situation calculus and its numerous extensions as well as the many competing proposals that it has inspired deal with this problem to some extent. In this paper, we offer a new approach to belief change associated with performing actions that addresses some of the shortcomings of these approaches. In particular, our approach is based on a well-developed theory of action in the situation calculus extended to deal with belief. Moreover, by augmenting this approach with a notion of plausibility over situations, our account handles nested belief, belief introspection, mistaken belief, and handles belief revision and belief update together with iterated belief change.

Dynamic belief revision operators

The AGM approach to belief change is not geared to provide a decent account of iterated belief change. Darwiche and Pearl have sought to extend the AGM proposal in an interesting way to deal with this problem. We show that the original Darwiche-Pearl approach is, on the one hand excessively strong and, on the other rather limited in scope. The later Darwiche-Pearl approach, we argue, although it addresses the first problem, still remains rather permissive. We address both these issues by (1) assuming a dynamic revision operator that changes to a new revision operator after each instance of belief change, and (2) strengthening the Darwiche-Pearl proposal. Moreover, we provide constructions of this dynamic revision operator via entrenchment kinematics as well as a simple form of lexicographic revision, and prove representation results connecting these accounts.

Severe Withdrawal (and Recovery)

The problem of how to remove information from an agents stock of beliefs is of paramount concern in the belief change literature. An inquiring agent may remove beliefs for a variety of reasons: a belief may be called into doubt or the agent may simply wish to entertain other possibilities. In the prominent AGM framework for belief change, upon which the work here is based, one of the three central operations, contraction, addresses this concern (the other two deal with the incorporation of new information). Makinson has generalised this work by introducing the notion of a withdrawal operation. Underlying the account proffered by AGM is the idea of rational belief change. A belief change operation should be guided by certain principles or integrity constraints in order to characterise change by a rational agent. One of the most noted principles within the context of AGM is the Principle of Informational Economy. However, adoption of this principle in its purest form has been rejected by AGM leading to a more relaxed interpretation. In this paper, we argue that this weakening of the Principle of Informational Economy suggests that it is only one of a number of principles which should be taken into account. Furthermore, this weakening points toward a Principle of Indifference. This motivates the introduction of a belief removal operation that we call severe withdrawal. We provide rationality postulates for severe withdrawal and explore its relationship with AGM contraction. Moreover, we furnish possible worlds and epistemic entrenchment semantics for severe withdrawals.

Basic behaviour control of the vision‐based cognitive robotic disassembly automation

Purpose – The purpose of this paper is to develop an automated disassembly cell that is flexible and robust to the physical variations of a product. In this way it is capable of dealing with any model of product, regardless of the level of detail in the supplied information.Design/methodology/approach – The concept of cognitive robotics is used to replicate human level expertise in terms of perception and decision making. As a result, difficulties with respect to the uncertainties and variations of the product in the disassembly process are resolved.Findings – Cognitive functions, namely reasoning and execution monitoring, can be used in basic behaviour control to address problems in variations of the disassembly process due to variations in the products structure particularly across different models of the product.Research limitations/implications – The paper provides a practical approach to formulating the disassembly domain and behaviour control of the cognitive robotic agent via a high‐level logical ...

Horn contraction via epistemic entrenchment

Belief change studies the way in which a reasoner should maintain its beliefs in the face of newly acquired information. The AGM account of belief change assumes an underlying logic containing classical propositional logic. Recently, there has been interest in studying belief change, specifically contraction, under the Horn fragment of propositional logic (i.e., Horn logic). In this paper we continue this line of research, and propose a Horn contraction that is based on the Epistemic Entrenchment (EE) construction of AGM contraction. The standard EE construction refers to arbitrary disjunctions which are not available in Horn logic. Therefore, we make use of a Horn approximation technique called Horn strengthening. An ideal Horn contraction should be as plausible as an AGM contraction. In other words it should performs identically with AGM contractions when restricted to Horn logic. We demonstrate that no EE based Horn contraction satisfies this criterion unless we apply certain restrictions to the AGM contraction. A representation theorem is proved which identifies the characterising postulates for our Horn contraction.

Evaluating Sequential Single-Item Auctions for Dynamic Task Allocation

Sequential single-item (SSI) auctions have proven a very effective technique for tackling static task allocation problems in multi-robot settings and are only recently being applied to dynamic task allocation problems. We complement existing work by evaluating the effects of using different auctioning and winner determination schemes when dealing with dynamically appearing tasks. To this end we investigate the use of plan modification versus re-planning and minimum cost and regret clearing for winner determination in the auction.

Implementing iterated belief change via prime implicates

Belief change is concerned with modelling the way in which an idealised (rational) reasoner maintains their beliefs and the way in which those beliefs are modified as the reasoner acquires new information. The AGM [1,3,5] framework is the most widely cited belief change methodology in the literature. It models the reasoner’s belief state as a set of sentences that is logically closed under deduction and provides for three belief change operations: expansion, contraction and revision. Each of the AGM belief change operations is motivated by principles of rationality that are formalised by way of rationality postulates.

Transitively relational partial meet horn contraction

Following the recent trend of studying the theory of belief revision under the Horn fragment of propositional logic this paper develops a fully characterised Horn contraction which is analogous to the traditional transitively relational partial meet contraction [Alchourron et al., 1985]. This Horn contraction extends the partial meet Horn contraction studied in [Delgrande and Wassermann, 2010] so that it is guided by a transitive relation that models the ordering of plausibility over sets of beliefs.

A Framework for Using Cognitive Robotics in Disassembly Automation

Most of disassembly has been carried out manually due to the uncertainties associated with the quality and the quantity of the products returned. This has been a hindrance for automation of disassembly. In this research, the concept of “Cognitive robotics” is proposed to address these problems. Cognitive robotics is an autonomous robot equipped with cognitive functionalities allowing the system to interact with the conditions occurring in a dynamic domain. This article proposes a framework of implementing cognitive robotics on a vision-based disassembly cell. Consequently, the system can deal with any product model in one product group regardless of their specific structure and geometrical detail.

Knowledge compilation for belief change

Techniques for knowledge compilation like prime implicates and binary decision diagrams (BDDs) are effective methods for improving the practical efficiency of reasoning tasks. In this paper we provide a construction for a belief contraction operator using prime implicates. We also briefly indicate how this technique can be used for belief expansion, belief revision and also iterated belief change. This simple yet novel technique has two significant features: (a) the contraction operator constructed satisfies all the AGM postulates for belief contraction; (b) when compilation has been effected only syntactic manipulation is required in order to contract the reasoners belief state.