Rational belief revision is commonly viewed as being based on a preference order between possible worlds, with the resulting new belief set being those sentences true in all the most preferred models of the incoming new information. Usually, such a preference order is taken to be a total preorder. Nevertheless, there are other, more general classes of ordering that can also be employed. In this paper, we explore two such classes that have been studied within the theory of rational choice but have seen limited or no application in belief revision. We begin with interval orders, introduced by Fishburn in the ’80s, which associate to each possible world a nonnegative ‘interval’ of plausibility. We then move on to biorders, studied by Aleskerov, Bouyssou, and Monjardet, which generalise interval orders by allowing the intervals to have negative lengths, a feature that can be used to capture a notion of dissonance or instability. We provide axiomatic characterisations of these two resulting families of belief revision operators, as well as of two further families of interest that lie between interval orders and biorders. We show that while biorder-based revisions satisfy the Success postulate, they do not always yield consistent outputs. By modifying their definition to discard inputs that lead to inconsistency as ‘incredible’, we derive new families of so-called nonprioritised revision that satisfy the Consistency postulate, but not the Success one. These families are linked to credibility-limited revision operators of Hansson et al., but for which the set of credible sentences does not satisfy the single-sentence closure condition. We argue that the biorder-based approach is well-suited for scenarios where an agent might initially reject new information, but may accept it when presented with additional explanation.

Despite efforts to better understand the constraints that operate on single-step parallel (aka ``package'', ``multiple'') revision, very little work has been carried out on how to extend the model to the iterated case. A recent paper by Delgrande & Jin outlines a range of relevant rationality postulates. While many of these are plausible, they lack an underlying unifying explanation. We draw on recent work on iterated parallel contraction to offer a general method for extending serial iterated belief revision operators to handle parallel change. This method, based on a family of order aggregators known as TeamQueue aggregators, provides a principled way to recover the independently plausible properties that can be found in the literature, without yielding the more dubious ones.

The standard ``serial'' (aka ``singleton'') model of belief contraction models the manner in which an agent's corpus of beliefs responds to the removal of a single item of information. One salient extension of this model introduces the idea of ``parallel'' (aka ``package'' or ``multiple'') change, in which an entire set of items of information are simultaneously removed. Existing research on the latter has largely focussed on single-step parallel contraction: understanding the behaviour of beliefs after a single parallel contraction. It has also focussed on generalisations to the parallel case of serial contraction operations whose characteristic properties are extremely weak. Here we consider how to extend serial contraction operations that obey stronger properties. Potentially more importantly, we also consider the iterated case: the behaviour of beliefs after a sequence of parallel contractions. We propose a general method for extending serial iterated belief change operators to handle parallel change based on an n-ary generalisation of Booth & Chandler's TeamQueue binary order aggregators.

Inductive inference is a well-studied form of nonmonotonic reasoning in which various inference is based on conditional belief bases rather than belief bases consisting of classical logic statements. Given its nonmonotonic nature, many important logical properties that are taken for granted in the classical case do not necessarily carry over to inference involving conditionals. In this paper we consider two such properties---equivalence and language-independence. More specifically, we provide different notions of equivalence in the conditional case, and show which of these are satisfied by which forms of conditional inference. Similarly, we consider different versions of language independence, and test various forms of conditional inference against these. As its main overall contribution, the paper provides deeper theoretical insights into the field of inductive inference.

One of the most widely investigated decision problems in symbolic AI is that of which conditional sentences of the form “if α , then normally β ” should follow from a knowledge base containing this type of statements. Probably, the most notable approach to this problem is the rational closure construction put forward by Lehmann and Magidor in the'90s, which has been adapted to logical languages of various expressive powers since then. At the core of rational closure is the Rational Monotonicity property, which allows one to retain existing (defeasible) conclusions whenever new information cannot be negated by existing conclusions. As it turns out, Rational Monotonicity is not universally accepted, with many researchers advocating the investigation of weaker versions thereof leading to a larger class of consequence relations. A case in point is that of the Disjunctive Rationality property, which states that if one may draw a (defeasible) conclusion from a disjunction of premises, then one should be able to draw this conclusion from at least one of the premises taken alone. While there are convincing arguments that the rational closure forms the ‘simplest’ rational consequence relation extending a given set of conditionals, the question of what the simplest disjunctive consequence relation in this setting is has not been explored in depth. In this article, we do precisely that by motivating and proposing a concrete construction of the disjunctive rational closure of a conditional knowledge base, of which the properties and consequences of its adoption we also investigate in detail. (Previous versions of this work have been selected for presentation at the 18th International Workshop on Nonmonotonic Reasoning (NMR 2020) [1] and at the 35th AAAI Conference on Artificial Intelligence (AAAI 2021) [2] . The present submission extends and elaborates on both papers.)

Natural language offers an appealing alternative to formal logics as a vehicle for representing knowledge. However, using natural language means that standard methods for automated reasoning can no longer be used. A popular solution is to use transformer-based language models (LMs) to directly reason about knowledge expressed in natural language, but this has two important limitations. First, the set of premises is often too large to be directly processed by the LM. This means that we need a retrieval strategy which can select the most relevant premises when trying to infer some conclusion. Second, LMs have been found to learn shortcuts and thus lack robustness, putting in doubt to what extent they actually understand the knowledge that is expressed. Given these limitations, we explore the following alternative: rather than using LMs to perform reasoning directly, we use them to learn embeddings of individual assertions. Reasoning is then carried out by manipulating the learned embeddings. We show that this strategy is feasible to some extent, while at the same time also highlighting the limitations of directly fine-tuning LMs to learn the required embeddings.

Truth discovery networks evaluate the trustworthiness of sources (e.g., websites) and their claims (e.g., the severity of a virus). Intuitively, the more trustworthy the sources of a claim, the more believable the claim and vice versa. Singleton noted that bipolar abstract argumentation could be a natural way to reason about these networks. We explain how this idea can be implemented naturally by quantitative bipolar argumentation frameworks (QBAFs) that we call TD-QBAFs. While most applications of QBAFs result in a (nearly) acyclic structure, TD-QBAFs have bi-directional edges and can feature complex cycles. The stability (convergence behaviour) of QBAFs in cyclic graphs is currently not well understood. While pathological examples of divergent QBAFs have been constructed, the problems seemed unlikely to occur in practice. However, convergence problems seem to be the rule rather than the exception for TD-QBAFs. We demonstrate how common QBAF semantics can fail to converge for very simple TD-QBAFs and discuss some of the potential causes. While this shows limitations of existing semantics, we also discuss how some previously proposed ideas can be used to mitigate the problems and demonstrate their effectiveness empirically.

Quantitative bipolar argumentation frameworks (QBAFs) have various applications in areas like product recommendation, review aggregation and explaining machine learning models. QBAF semantics assign a strength to every argument that is based on an a priori belief and the strength of its attackers and supporters. Intuitively, a QBAF semantics is open-minded when it is unbiased in the sense that a priori beliefs can be given up eventually when sufficient arguments to the contrary are presented. While this behaviour is desirable in many applications, existing open-minded semantics also have the property that even very weak arguments will eventually eliminate the a priori beliefs. In this paper, we will study notions of conservativeness that demand that the deviation from the a priori beliefs is bounded by the strength of pro and contra arguments. We will discuss compatibility and conflicts with existing properties and present two new semantics with interesting semantical guarantees. To do so, we will build up on the framework of modular semantics and prove some general relationships between functional and semantical properties that are useful to simplify the study of new modular semantics.

We study what can be learned when receiving propositional reports from multiple nonexpert information sources. We suppose that sources report all that they consider possible, given their expertise. This may result in false and inconsistent reports when sources lack expertise on a topic. A learning method is truth-tracking, roughly speaking, if it eventually converges to correct beliefs about the “actual” world. This involves finding both the actual state of affairs in the domain described by the sources, and finding the extent of the expertise of the sources themselves. We investigate the extent to which truth-tracking is possible, and describe what information can be learned even if the actual world cannot be pinned down uniquely. We find that a broad spread of expertise among the sources allows the actual state of affairs to be found, even if no individual source is an expert on all topics. On the other hand, narrower expertise at the individual level allows the actual expertise to be found more easily. Finally, we turn to learning methods themselves: we provide a postulate-based characterisation of truth-tracking for general methods under mild assumptions, before looking at a couple of specific classes of methods from the belief change literature.

Abstract The problem of truth discovery, i.e., of trying to find the true facts concerning a number of objects based on reports from various information sources of unknown trustworthiness, has received increased attention recently. The problem is made interesting by the fact that the relative believability of facts depends on the trustworthiness of their sources, which in turn depends on the believability of the facts the sources report. Several algorithms for truth discovery have been proposed, but their evaluation has mainly been performed experimentally by computing accuracy against large datasets. Furthermore, it is often unclear how these algorithms behave on an intuitive level. In this paper we take steps towards a framework for truth discovery which allows comparison and evaluation of algorithms based instead on their theoretical properties. To do so we pose truth discovery as a social choice problem, and formulate various axioms that any reasonable algorithm should satisfy. Along the way we provide an axiomatic characterisation of the baseline ‘Voting’ algorithm—which leads to an impossibility result showing that a certain combination of the axioms cannot hold simultaneously—and check which axioms a particular well-known algorithm satisfies. We find that, surprisingly, our more fundamental axioms do not hold, and propose modifications to the algorithms to partially fix these problems.

The question of conditional inference, i.e., of which conditional sentences of the form ``if A then, normally, B'' should follow from a set KB of such sentences, has been one of the classic questions of AI, with several well-known solutions proposed. Perhaps the most notable is the rational closure construction of Lehmann and Magidor, under which the set of inferred conditionals forms a rational consequence relation, i.e., satisfies all the rules of preferential reasoning, *plus* Rational Monotonicity. However, this last named rule is not universally accepted, and other researchers have advocated working within the larger class of *disjunctive* consequence relations, which satisfy the weaker requirement of Disjunctive Rationality. While there are convincing arguments that the rational closure forms the ``simplest'' rational consequence relation extending a given set of conditionals, the question of what is the simplest *disjunctive* consequence relation has not been explored. In this paper, we propose a solution to this question and explore some of its properties.

The belief revision literature has largely focussed on the issue of how to revise one’s beliefs in the light of information regarding matters of fact. Here we turn to an important but comparatively neglected issue: How to model agents capable of acquiring information regarding which rules of inference (‘Ramsey Test conditionals’) they ought to use in reasoning about these facts. Our approach to this second question of so-called ‘conditional revision’ is distinctive insofar as it abstracts from the controversial details of how the address the first. We introduce a ‘plug and play’ method for uniquely extending any iterated belief revision operator to the conditional case. The flexibility of our approach is achieved by having the result of a conditional revision by a Ramsey Test conditional (‘arrow’) determined by that of a plain revision by its corresponding material conditional (‘hook’). It is shown to satisfy a number of new constraints that are of independent interest.

Belief revision is concerned with incorporating new information into a pre-existing set of beliefs. When the new information comes from another agent, we must first determine if that agent should be trusted. In this paper, we define trust as a pre-processing step before revision. We emphasize that trust in an agent is often restricted to a particular domain of expertise. We demonstrate that this form of trust can be captured by associating a state partition with each agent, then relativizing all reports to this partition before revising. We position the resulting family of trust-sensitive revision operators within the class of selective revision operators of Ferme and Hansson, and we prove a representation result that characterizes the class of trust-sensitive revision operators in terms of a set of postulates. We also show that trust-sensitive revision is manipulable, in the sense that agents can sometimes have incentive to pass on misleading information.

Given a set of conflicting arguments, there can exist multiple plausible opinions about which arguments should be accepted, rejected or deemed undecided. We study the problem of how multiple such judgements can be aggregated. We define the problem by adapting various classical social-choice-theoretic properties for the argumentation domain. We show that while argument-wise plurality voting satisfies many properties, it fails to guarantee the collective rationality of the outcome. We then present more general results, proving multiple impossibility results on the existence of any good aggregation operator. After characterizing the sufficient and necessary conditions for satisfying collective rationality, we study whether restricting the domain of argument-wise plurality voting to classical semantics allows us to escape the impossibility result. We close by mentioning a couple of graph-theoretical restrictions under which the argument-wise plurality rule does produce collectively rational outcomes. In addition to identifying fundamental barriers to collective argument evaluation, our results contribute to research at the intersection of the argumentation and computational social choice fields.

The current paper provides a dialectical interpretation of the argumentation-based judgment aggregation operators of Caminada and Pigozzi. In particular, we define discussion-based proof procedures for the foundational concepts of down-admissible and up-complete. We then show how these proof procedures can be used as the basis of dialectical proof procedures for the sceptical, credulous and super credulous judgment aggregation operators.

In an abstract argumentation framework, there are often multiple plausible ways to evaluate (or label) the status of each argument as accepted, rejected, or undecided. But often there exists a critical set of arguments whose status is sufficient to determine uniquely the status of every other argument. Once an agent has decided its position on a critical set of arguments, then essentially the entire frame-work has been evaluated. Likewise, once a group, e.g. a jury, agrees on the status of a critical set of arguments, all of their different views over all other arguments are resolved. Thus, critical sets of arguments are important both for efficient evaluation by individual agents and for collective agreement by groups of such. To exploit this idea in practice, however, a number of computational questions must be considered. In particular, how much computational effort is needed to verify that a set is, indeed, a critical set or a minimal critical set. In this paper we determine exact bounds on the computational complexity of these and related questions. In addition we provide similar analyses of issues: a concept closely related to critical set and derived in terms of (equivalence) classes of arguments related through “common” labelling behaviours.

In this paper we introduce and study credibility-limited improvement operators. The idea is to accept the new piece of information if this information is judged credible by the agent, so in this case a revision is performed. When the new piece of information is not credible then it is not accepted (no revision is performed), but its plausibility is still improved in the epistemic state of the agent, similarly to what is done by improvement operators. We use a generalized definition of Darwiche and Pearl epistemic states, where to each epistemic state can be associated, in addition to the set of accepted formulas (beliefs), a set of credible formulas. We provide a syntactic and semantic characterization of these operators.

We develop a model of abduction in abstract argumentation, where changes to an argumentation framework act as hypotheses to explain the support of an observation. We present dialogical proof theories for the main decision problems (i.e., finding hypotheses that explain skeptical/credulous support) and we show that our model can be instantiated on the basis of abductive logic programs.

Dung-style abstract argumentation theory centers on argumentation frameworks and acceptance functions. The latter take as input a framework and return sets of labelings. This methodology assumes full awareness of the arguments relevant to the evaluation. There are two reasons why this is not satisfactory. Firstly, full awareness is, in general, not a realistic assumption. Second, frameworks have explanatory power, which allows us to reason abductively or counterfactually, but this is lost under the usual semantics. To recover this aspect, we generalize conventional acceptance, and we present the concept of a conditional acceptance function.

We show that Booth et al.'s Horn contraction based on infra-remainder sets corresponds exactly to kernel contraction for belief sets. This result is obtained via a detour through Horn contraction for belief bases, which supports the conjecture that Horn belief change is best viewed as a “hybrid” version of belief set change and belief base change. Moreover, the link with base contraction gives us a more elegant representation result for Horn contraction for belief sets in which a version of the Core-retainment postulate features.

We look at the problem in belief revision of trying to make inferences about what an agent believed—or will believe—at a given moment, based on an observation of how the agent has responded to some sequence of previous belief revision inputs over time. We adopt a ‘reverse engineering’ approach to this problem. Assuming a framework for iterated belief revision which is based on sequences, we construct a model of the agent that ‘best explains’ the observation. Further considerations on this best-explaining model then allow inferences about the agent's epistemic behaviour to be made. We also provide an algorithm which computes this best explanation.

As partial justification of their framework for iterated belief revision Darwiche and Pearl convincingly argued against Boutilier's natural revision and provided a prototypical revision operator that fits into their scheme. We show that the Darwiche-Pearl arguments lead naturally to the acceptance of a smaller class of operators which we refer to as admissible. Admissible revision ensures that the penultimate input is not ignored completely, thereby eliminating natural revision, but includes the Darwiche-Pearl operator, Nayak's lexicographic revision operator, and a newly introduced operator called restrained revision. We demonstrate that restrained revision is the most conservative of admissible revision operators, effecting as few changes as possible, while lexicographic revision is the least conservative, and point out that restrained revision can also be viewed as a composite operator, consisting of natural revision preceded by an application of a "backwards revision" operator previously studied by Papini. Finally, we propose the establishment of a principled approach for choosing an appropriate revision operator in different contexts and discuss future work.

We provide a formal study of belief retraction operators that do not necessarily satisfy the (Inclusion) postulate. Our intuition is that a rational description of belief change must do justice to cases in which dropping a belief can lead to the inclusion, or ‘liberation’, of others in an agent’s corpus. We provide two models of liberation via retraction operators: σ-liberation and linear liberation. We show that the class of σ-liberation operators is included in the class of linear ones and provide axiomatic characterisations for each class. We show how any retraction operator (including the liberation operators) can be ‘converted’ into either a withdrawal operator (i.e., satisfying (Inclusion)) or a revision operator via (a slight variant of) the Harper Identity and the Levi Identity respectively.