Abstract Indirect reciprocity, supported by simple reputation assessment and social norms, has been demonstrated as an effective mechanism for enabling cooperation in populations of self-interested individuals. However, it has been shown that where there is noise in the performance of actions, or in observers’ perceptions, cooperation may not emerge. Higher-order social norms and generosity have been investigated as potential mechanisms to support cooperation in such environments, but are ineffective without additional, limiting, assumptions. In particular, higher-order norms have typically been investigated in cases where reputation is binary (‘good’ or ‘bad’) and where all agents ascribe the same reputation to an individual, implying full and perfect observation of actions. Generosity with an ‘aligned’ strategy, where all agents have the same likelihood of being generous, has been shown to be ineffective where reputation is binary. In this work we consider reciprocity emergence mechanisms in noisy domains and where agent observations may be incomplete or inaccurate. Our hypothesis is that nuanced reputation scores will enable generosity to be more effective, since an individual act of generosity will have a less extreme impact on reputation. We also investigate whether replacing the ‘aligned’ setting for generosity with a ‘non-aligned’ alternative, which we refer to as forgiveness, will support cooperation in noisy partially observable environments, without the level of ‘unjustified benevolence’ exhibited by generosity. We show both analytically and empirically that generosity when combined with fine grained reputation can help cooperation emerge, and that forgiveness can support cooperation in certain settings.

Weighted gradual semantics provide an acceptability degree to each argument representing its final strength, computed based on factors including the argument's background evidence, and taking into account interactions between the argument and others. We introduce five important problems linking gradual semantics and acceptability degrees. First, we re-examine the inverse problem, seeking to identify each argument's initial weights within the argumentation framework which lead to a specific final acceptability degree. Second, we ask whether the function mapping between argument weights and acceptability degrees is one-to-one. Third, we ask if this mapping is a homeomorphism so that small perturbations in weights lead to small perturbation in acceptability degrees and vice versa. Fourth, we ask whether argument weights can be found when preferences, rather than acceptability degrees for arguments are considered. Last, we consider the geometry of the space of valid acceptability degrees, asking whether ``"gaps" exist in this space. While different gradual semantics have been proposed in the literature, in this paper, and building on the geometry of the acceptability degree space, we identify a large family of weighted gradual semantics which contains many of the existing well-known semantics while maintaining desirable properties such as convergence to a unique fixed point and solving all five aforementioned problems.

Responsibility plays a key role in the development and deployment of trustworthy autonomous systems. In this paper, we focus on the problem of strategic reasoning in probabilistic multi-agent systems with responsibility-aware agents. We introduce the logic PATL+R, a variant of Probabilistic Alternating-time Temporal Logic. The novelty of PATL+R lies in its incorporation of modalities for causal responsibility, providing a framework for responsibility-aware multi-agent strategic reasoning. We present an approach to synthesise joint strategies that satisfy an outcome specified in PATL+R, while optimising the share of expected causal responsibility and reward. This provides a notion of balanced distribution of responsibility and reward gain among agents. To this end, we utilise the Nash equilibrium as the solution concept for our strategic reasoning problem and demonstrate how to compute responsibility-aware Nash equilibrium strategies via a reduction to parametric model checking of concurrent stochastic multi-player games.

Research has shown that cooperative action struggles to emerge in the noisy variant of the donation game, a simple model of noisy multi-agent systems where indirect reciprocity is required to maximise utility. Such noise can arise when agents may have an incorrect view of the reputation of their interaction partners, or when the actions themselves may fail. Concepts such as generosity, as well as the use of higher-order norms, have been investigated as mechanisms to facilitate cooperation in such environments, but often are not effective or require additional assumptions or infrastructure in the system to operate. In this paper, we demonstrate both analytically and empirically that a simple form of generosity when combined with fine grained reputation can help cooperation emerge. We also show that the use of individual forgiveness strategies rather than the presence of global generosity can support cooperation in such environments.

Gradual semantics with abstract argumentation provide each argument with a score reflecting its acceptability. Many different gradual semantics have been proposed in the literature, each following different principles and producing different argument rankings. A sub-class of such semantics, the so-called weighted semantics, takes, in addition to the graph structure, an initial set of weights over the arguments as input, with these weights affecting the resultant argument ranking. In this work, we consider the inverse problem over such weighted semantics. That is, given an argumentation framework and a desired argument ranking, we ask whether there exist initial weights such that a particular semantics produces the given ranking. The contribution of this paper are: (1) an algorithm to answer this problem, (2) a characterisation of the properties that a gradual semantics must satisfy for the algorithm to operate, and (3) an empirical evaluation of the proposed algorithm.

Abstract BDI agents act in response to external inputs and their internal plan library. Understanding the root cause of BDI agent action is often difficult, and in this paper we present a dialogue based approach for explaining the behaviour of a BDI agent. We consider two dialogue participants who may have different views regarding the beliefs, plans and external events which drove agent action (encoded via traces). These participants make utterances which incrementally reveal their traces to each other, allowing them to identify divergences in the traces, or to conclude that their traces agree. In practice, we envision a human taking on the role of a dialogue participant, with the BDI agent itself acting as the other participant. The dialogue then facilitates explanation, understanding and debugging of BDI agent behaviour. After presenting our formalism and its properties, we describe our implementation of the system and provide an example of its use in a simple scenario.

Abstract Probabilistic argumentation combines probability theory and formal models of argumentation. Given an argumentation graph where vertices are arguments and edges are attacks or supports between arguments, the approach of probabilistic labellings relies on a probability space where the sample space is any specific set of argument labellings of the graph, so that any labelling outcome can be associated with a probability value. Argument labellings can feature a label indicating that an argument is not expressed, and in previous work these labellings were constructed by exploiting the subargument-completeness postulate according to which if an argument is expressed then its subarguments are expressed and through the use of the concept of ‘subargument-complete subgraphs’. While the use of such subgraphs is interesting to compare probabilistic labellings with other works in the literature, it may also hinder the comprehension of a relatively simple framework. In this short communication, we revisit the construction of probabilistic labellings and demonstrate how labellings can be specified without reference to the concept of subargument-complete subgraphs. By doing so, the framework is simplified and yields a more natural model of argumentation.

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We propose a subjective Bayesian network approach for cybersecurity risk assessment to address the limitations of traditional risk assessment models, which use precise values for the likelihoods of cyber-attacks. In many situations, it is often difficult to elicit accurate probabilities due to lack of knowledge, or insufficient historical data, making the evaluation of risk in existing approaches unreliable. With this approach, we seek to better reflect the reality underpinning the model and offer a better approach to decision-making via the modelling of uncertainty about the probability distributions in the form of subjective opinions, resulting in a model taking second-order uncertainty into account. We develop a subjective Bayesian network for cybersecurity risk, and then discuss the risk evaluation and decision analysis problem under the proposed model. Finally, our approach is evaluated against classical Bayesian networks using the scenario of wiper malware in an industrial control system. Our results show that taking uncertainty about the probabilities into account during security risk analysis can lead to different outcomes, and therefore different security decisions.

In a normative environment, an agent’s actions are directed not only by its goals but also by the norms activated by its actions and those of other actors. The potential for conflict between agent goals and norms makes decision making challenging, in that it requires looking ahead to consider the longer-term consequences of which goal to satisfy or which norm to comply with in face of conflict. We therefore seek to determine the actions an agent should select at each point in time, taking account of its temporal goals, norms, and their conflicts. We propose a solution in which a normative planning problem is the basis for practical reasoning based on argumentation. Various types of conflict within goals, within norms, and between goals and norms are identified based on temporal properties of these entities. The properties of the best plan(s) with respect to goal achievement and norm compliance are mapped to arguments, followed by mapping their conflicts to attack between arguments, all of which are used to identify why a plan is justified.

Assessing whether an agent has abandoned a goal or is actively pursuing it is important when multiple agents are trying to achieve joint goals, or when agents commit to achieving goals for each other. Making such a determination for a single goal by observing only plan traces is not trivial, as agents often deviate from optimal plans for various reasons, including the pursuit of multiple goals or the inability to act optimally. In this article, we develop an approach based on domain independent heuristics from automated planning, landmarks, and fact partitions to identify sub-optimal action steps—with respect to a plan—within a fully observable plan execution trace. Such capability is very important in domains where multiple agents cooperate and delegate tasks among themselves, such as through social commitments , and need to ensure that a delegating agent can infer whether or not another agent is actually progressing towards a delegated task. We demonstrate how a creditor can use our technique to determine—by observing a trace—whether a debtor is honouring a commitment. We empirically show, for a number of representative domains, that our approach infers sub-optimal action steps with very high accuracy and detects commitment abandonment in nearly all cases.

To resolve conflicts amongst norms, various non-monotonic formalisms can be used to perform prioritized normative reasoning. Meanwhile, formal argumentation provides a way to represent non-monotonic logics. In this paper we propose a representation of prioritized normative reasoning by argumentation. Using hierarchical abstract normative systems (HANS), we define three kinds of prioritized normative reasoning approaches called Greedy, Reduction and Optimization. Then, after formulating an argumentation theory for a HANS, we show that for a totally ordered HANS, Greedy and Reduction can be represented in argumentation by applying the weakest link and the last link principles, respectively, and Optimization can be represented by introducing additional defeats capturing the idea that for each argument that contains a norm not belonging to the maximal obeyable set then this argument should be rejected.

Market mechanisms are now playing a key role in the allocation and pricing of on-demand transportation services. In practice, most such services use posted-price mechanisms, where both passengers and drivers are offered a journey price which they can accept or reject. However, providers such as Liftago and GrabTaxi have begun to adopt a mechanism whereby auctions are used to price drivers. These latter mechanisms are neither posted-price nor classical double auctions and can instead be considered a hybrid mechanism. In this paper, we describe and study the properties of a novel hybrid on-demand transport mechanism. As these mechanisms require knowledge of passenger demand, we analyze the data-profit tradeoff as well as how the passenger and driver preferences influence mechanism performance. We show that the revenue loss for the provider scales with √n log n for n passenger requests under a multi-armed bandit learning algorithm with beta-distributed preferences. We also investigate the effect of subsidies on both profit and the number of successful journeys allocated by the mechanism, comparing these with a posted-price mechanism, showing improvements in profit with a comparable number of successful requests.

Intentional engagement in positive activities, such as practicing kindness, showing generosity or expressing gratitude, can help people increase their happiness levels and improve their wellbeing. In this paper we explore how a gamified digital behaviour change intervention can be adapted to encourage people of different personality types to engage in simple acts of kindness. Participants were assigned 5 daily activities for 7 days, and asked to complete as many as possible by the end of each day. Participants received different persuasive notifications every day to encourage them to complete a higher number of activities. We investigated how participation levels are influenced by different personality types, different persuasive message types and different categories of activities. Furthermore, we analysed the influence of the intervention on participant behaviour and the effect on behavioural intention, by comparing pre-intention and post-intention to perform different kinds of positive activities. The findings from this study have implications for future work on personalising persuasive interventions to improve wellbeing and prevent mental health problems.

Automated planning can be used to efficiently recognize goals and plans from partial or full observed action sequences. In this paper, we propose goal recognition heuristics that rely on information from planning landmarks - facts or actions that must occur if a plan is to achieve a goal when starting from some initial state. We develop two such heuristics: the first estimates goal completion by considering the ratio between achieved and extracted landmarks of a candidate goal, while the second takes into account how unique each landmark is among landmarks for all candidate goals. We empirically evaluate these heuristics over both standard goal/plan recognition problems, and a set of very large problems. We show that our heuristics can recognize goals more accurately, and run orders of magnitude faster, than the current state-of-the-art.

We demonstrate an implementation of Markov Argumentation Random Fields (MARFs), a novel formalism combining elements of formal argumentation theory and probabilistic graphical models. In doing so MARFs provide a principled technique for the merger of probabilistic graphical models and non-monotonic reasoning, supporting human reasoning in ``messy’’ domains where the knowledge about conflicts should be applied. Our implementation takes the form of a graphical tool which supports users in interpreting complex information. We have evaluated our implementation in the domain of intelligence analysis, where analysts must reason and determine likelihoods of events using information obtained from conflicting sources.

In this paper we combine fuzzy set theory and argumentation to facilitate the use of fuzzy arguments and attacks. Unlike many existing approaches, our work does not require the use of any parameters, bringing it closer to Dung's work in spirit. We begin by introducing Fuzzy Argumentation Frameworks, and specialise them using the Gödel t-norm. We then examine this framework's properties and show that the standard Dung extensions are obtained, though the stable semantics coincide with the preferred. Finally, we examine the relationship between our framework and Dung's original system, as well as the existing fuzzy frameworks, describing where they overlap and differ.

When entering a system, an agent should be aware of the obligations and prohibitions (collectively norms) that affect it. Existing solutions to this norm identification problem make use of observations of either norm compliant, or norm violating, behaviour. Thus, they assume an extreme situation where norms are typically violated, or complied with. In this paper we propose a Bayesian approach to norm identification which operates by learning from both norm compliant and norm violating behaviour. We evaluate our approach's effectiveness empirically and compare its accuracy to existing approaches. By utilising both types of behaviour, we not only overcome a major limitation of such approaches, but also obtain improved performance over the state of the art, allowing norms to be learned with fewer observations.

Social isolation among older adults represents a significant societal challenge in which persuasion offers a potential solution. To develop a persuasive interactive system for this purpose, we conducted a modeling study with carers to discover how persuasion is used to encourage social interaction among older adults. From an analysis of the results, we identified and defined effective persuasive strategies grounded in theories of persuasion and developed a computational model for applying them. This article reports the findings from an evaluation of the generalizability of this model and presents a revised version based on these results. The article concludes with a discussion on possible domain-specific conceptual features between the model evaluated and the revised model developed.

The work of Caminada & Amgoud presents two possible ways of satisfying the rationality postulates: one using restricted rebut, and one using unrestricted rebut. Subsequent work on ASPIC+ has extended the work of Caminada & Amgoud, for instance by allowing preferences over arguments. However, such extensions have utilised restricted rebut only. In the current paper, we make the case for unrestricted rebut, and provide a formalism (called ASPIC−) that implements preferences between the defeasible rules, in the context of unrestricted rebut while still satisfying the rationality postulates of Caminada & Amgoud.

We introduce probo, a general benchmark framework for comparing abstract argumentation solvers. probo is intended to act as the core of an argumentation competition intended to run in 2015.

Dung's original argumentation frameworks have been extended in various ways. One such extension introduces positive interactions, or support, between arguments. Frameworks containing evidential, necessary, and deductive supports have been proposed, and it is natural to compare these and analyse whether translations between these are possible. Although a positive answer was given in the necessary and deductive cases, it was claimed that evidential support cannot be expressed by any other type and that it cannot be handled together with them in a single framework. In this paper we show that it is not the case and that there exists a natural translation between argumentation frameworks with necessities and evidential argumentation systems.

We present the CISpaces framework, a collaborative virtual space for intelligence analysts for the elaboration of information to explain a situation. CISpaces supports the analysis of conflicting information in collaboration exploiting argumentation schemes to structure and share analyses, crowd-sourcing to collect information and provenance to establish the credibility of hypotheses.

It has been claimed that computational models of argumentation provide support for complex decision making activities in part due to the close alignment between their semantics and human intuition. In this paper we assess this claim by means of an experiment: people's evaluation of formal arguments — presented in plain English — is compared to the conclusions obtained from argumentation semantics. Our results show a correspondence between the acceptability of arguments by human subjects and the justification status prescribed by the formal theory in the majority of the cases. However, post-hoc analyses show that there are some significant deviations, which appear to arise from implicit knowledge regarding the domains in which evaluation took place. We argue that in order to create argumentation systems, designers must take implicit domain specific knowledge into account.

Taxi journeys are usually priced according to the distance covered and time taken for the trip. Such a fixed cost strategy is simple to understand, but does not take into account the likelihood that a taxi can pick up additional passengers at the original passenger's destination. In this paper we investigate dynamic taxi pricing strategies. By using domain knowledge, such strategies discount trips to locations containing many potential passengers, and increase fares to those areas with few potential passengers. Identifying a closed form optimal dynamic pricing strategy is difficult, and by representing the domain as an MDP, we can identify an optimal strategy for specific domains. We empirically compare such dynamic pricing strategies with fixed cost strategies, and suggest future extensions to this work.

In this paper we present the EcoBioCap project and the modelling needs of this project in terms of argumentation based preference aggregation. The aim of the paper is to well describe the problem encountered in this context and to propose a preference logic in line with the expressivity needed by the application. We then show how to embed this logic within the ASPIC+ system. Finally, we show how argument by expert opinion could be integrated within our framework where preference aggregation needs to take into consideration the different expertise of the project stakeholders.

In this paper we examine the problem of practical reasoning utilising Atkinson et al.'s argument scheme in situations where differences as to the desirability of social value promotion exists. We focus on the situation where a single speaker attempts to persuade a set of listeners to undertake an action, assuming that the speaker has only a probabilistic model of the listener's mental state. To achieve this, we provide a new mapping from Atkinson's argumentation scheme to a VAF, introducing a new epistemic level to the VAF. We then present a simple protocol for the interaction between a single speaker and the set of listeners. Finally, situations in which strategic reasoning is necessary for the speaker are identified.

The specification of acceptable behaviour can be achieved via the use of obligations, permissions and prohibitions, collectively known as norms, which identify the states of affairs that should, may, or should not hold. Norms provide the ability to constrain behaviour while preserving individual agent autonomy. While much work has focused on the semantics of norms, the design of normative systems, and in particular understanding the impact of norms on a system, has received little attention. Since norms often interact with each other (for example, a permission may temporarily derogate an obligation, or a prohibition and obligation may conflict), understanding the effects of norms and their interactions becomes increasingly difficult as the number of norms increases. Yet this understanding can be critical in facilitating the design and development of effective or efficient systems. In response, this paper addresses the problem of norm explanation for Naïve users by providing of a graphical norm representation that can explicate why a norm is applicable, violated or complied with, and identify the interactions between permissions and other types of norms. We adopt a conceptual graph based semantics to provide this graphical representation while maintaining a formal semantics.

Abstract argument frameworks have been used for various applications within multi-agent systems, including reasoning and negotiation. Different argument frameworks make use of different inter-argument relations and semantics to identify some subset of arguments as coherent, yet there is no easy way to map between these frameworks; most commonly, this is done manually according to human intuition. In response, in this paper, we show how a set of arguments described using Dung's or Nielsen's argument frameworks can be mapped from and to an argument framework that includes both attack and support relations. This mapping preserves the framework's semantics in the sense that an argument deemed coherent in one framework is coherent in the other under a related semantics. Interestingly, this translation is not unique, with one set of arguments in the support based framework mapping to multiple argument sets within the attack only framework. Additionally, we show how EAF can be mapped into a subset of the argument interchange format (AIF). By using this mapping, any other argument framework using this subset of AIF can be translated into a DAF while preserving its semantics.