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Multi-agent systems is an evolving discipline that encompasses many different branches of research. The long-standing Agents at Aberdeen ( A 3 ) group undertakes research across several areas of multi-agent systems, focusing in particular on aspects related to resilience, reliability, and coordination. In this article we introduce the group and highlight past research successes in those themes, building a picture of the strengths within the group. We close the paper outlining the future direction of the group and identify key open challenges and our vision towards solving them.

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.

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.

What does it mean that something is probably obligatory? And how does it relate to the probability that it is permitted or prohibited? In this paper, we provide a possible answer by merging deontic argumentation and probabilistic argumentation into a probabilistic deontic argumentation framework. This framework allows us to specify a semantics for the probability of deontic statuses. The deontic argumentation part builds on standard concepts from the study of computational models of argument: rule-based arguments, argumentation graphs, argument labelling semantics and statement labelling semantics. We then encapsulate this deontic composition with the approach of probabilistic labellings to probabilistic argumentation, in order to associate deontic statements with probability values. The framework is illustrated with a scenario featuring a violation and a contrary-to-duty obligation.

If artificial intelligence (AI) is to help solve individual, societal and global problems, humans should neither underestimate nor overestimate its trustworthiness. Situated in-between these two extremes is an ideal ‘Goldilocks’ zone of credibility. But what will keep trust in this zone? We hypothesise that this role ultimately falls to the social cognition mechanisms which adaptively regulate conformity between humans. This novel hypothesis predicts that human-like functional biases in conformity should occur during interactions with AI. We examined multiple tests of this prediction using a collaborative remembering paradigm, where participants viewed household scenes for 30 s vs. 2 min, then saw 2-alternative forced-choice decisions about scene content originating either from AI- or human-sources. We manipulated the credibility of different sources (Experiment 1) and, from a single source, the estimated-likelihood (Experiment 2) and objective accuracy (Experiment 3) of specific decisions. As predicted, each manipulation produced functional biases for AI-sources mirroring those found for human-sources. Participants conformed more to higher credibility sources, and higher-likelihood or more objectively accurate decisions, becoming increasingly sensitive to source accuracy when their own capability was reduced. These findings support the hypothesised role of social cognition in regulating AI’s influence, raising important implications and new directions for research on human–AI interaction.

We propose a novel attack tree model, called a subjective attack tree, aiming to address the limitations of traditional attack trees, which use precise values for likelihoods of security events. 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. In this paper, we consider the modelling of uncertainty about probabilities, via subjective opinions, resulting in a model taking second-order uncertainty into account. We propose an approach to derive subjective opinions about security events based on two main criteria, namely a vulnerability level and technical difficulty to conduct an attack, using subjective logic. These subjective opinions are then used as input parameters in the proposed model. The propagation method of subjective opinions is also discussed. Our approach is evaluated against traditional attack trees using the Stuxnet self-installation scenario. Our results show that taking uncertainty about probabilities into account during security risk analysis can lead to different outcomes, and therefore different security decisions.

Subjective attack trees are an extension to traditional attack trees, proposed so to take uncertainty about likelihoods of security events into account during the modelling of security risk scenarios, using subjective opinions. This paper extends the work of subjective attack trees by allowing for the modelling of countermeasures, as well as conducting a comprehensive security and security investment analysis, such as risk measuring and analysis of profitable security investments. Our approach is evaluated against traditional attack trees. The results demonstrate the importance and advantage of taking uncertainty about probabilities into account. In terms of security investment, our approach seems to be more inclined to protect systems in presence of uncertainty (or lack of knowledge) about security events evaluations.

Various structured argumentation frameworks utilize preferences as part of their inference procedure. In this paper, we consider an inverse of the standard reasoning problem, seeking to identify what preferences could lead to a given set of conclusions being drawn. We ground our work in the Assumption-Based Argumentation (ABA) framework, and present an algorithm which computes and enumerates all possible sets of preferences (restricted to three identified cases) over the assumptions in the system from which a desired conflict-free set of conclusions can be obtained under a given semantics. After describing our algorithm, we establish its soundness, completeness and complexity.

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.

We describe a tool for providing explanation of plans to non-technical users, built on formal argumentation and dialogue theory, and supported by natural language generation and visualisation technologies. We describe how arguments can be generated from domain rules, and how justified arguments can be identified through dialogue, allowing the system to use such a dialogue to explain a plan. We provide information about our prototype system implementation, discussing its current limitations, and identifying potential avenues for future research.

Artificial intelligence has been increasing the autonomy of man-made artefacts such as software agents, self-driving vehicles and military drones. This increase in autonomy together with the ubiquity and impact of such artefacts in our daily lives have raised many concerns in society. Initiatives such as transparent and ethical AI aim to allay fears of a “free for all” future where amoral technology (or technology amorally designed) will replace humans with terrible consequences. We discuss the notion of accountable autonomy, and explore this concept within the context of practical reasoning agents. We survey literature from distinct fields such as management, healthcare, policy-making, and others, and differentiate and relate concepts connected to accountability. We present a list of justified requirements for accountable software agents and discuss research questions stemming from these requirements. We also propose a preliminary formalisation of one core aspect of accountability: responsibility.

Recognizing goals and plans from complete or partial observations can be efficiently achieved through automated planning techniques. In many applications, it is important to recognize goals and plans not only accurately, but also quickly. To address this challenge, we develop novel goal recognition approaches based on planning techniques that rely on planning landmarks. In automated planning, landmarks are properties (or actions) that cannot be avoided to achieve a goal. We show the applicability of a number of planning techniques with an emphasis on landmarks for goal recognition tasks in two settings: (1) we use the concept of landmarks to develop goal recognition heuristics; and (2) we develop a landmark-based filtering method to refine existing planning-based goal and plan recognition approaches. These recognition approaches are empirically evaluated in experiments over several classical planning domains. We show that our goal recognition approaches yield not only accuracy comparable to (and often higher than) other state-of-the-art techniques, but also result in substantially faster recognition time over existing techniques.

Within multi-agent systems, some agents may delegate tasks to other agents for execution. Recursive delegation designates situations where delegated tasks may, in turn, be delegated onwards. In unconstrained environments, recursive delegation policies based on quitting games are known to outperform policies based on multi-armed bandits. In this work, we incorporate allocation rules and rewarding schemes when considering recursive delegation, and reinterpret the quitting-game approach in terms of coalitions, employing the Shapley and Myerson values to guide delegation decisions. We empirically evaluate our extensions and demonstrate that they outperform the traditional multi-armed bandit based approach, while offering a resource efficient alternative to the quitting-game heuristic.

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.

Task delegation lies at the heart of the service economy, and is a fundamental aspect of many agent marketplaces. Research in computational trust considers which agent a task should be delegated to for execution given the agent’s past behaviour. However, such work does not consider the effects of the agent delegating the task onwards, forming a chain of delegations before the task is finally executed (as occurs in many human outsourcing scenarios). In this paper we consider such delegation chains, and empirically demonstrate that existing trust based approaches do not handle these situations as well. We then introduce a new algorithm based on quitting games to cater for recursive delegation.

Data sharing is becoming an integral part of many aspects of our daily lives. We propose a method for controlling access to data and knowledge through fine-grained, user-specified explicitly represented policies. We present an overview of a policy formalism and mechanisms to facilitate distributed data sharing. We provide a breakdown of how our approach defines compliance and violation, specifically providing a new outlook on violation of permissions within the context of data sharing. We also examine how our mechanisms have been adapted to support socially responsible interactions between participants, whilst still providing them with control over their own data. We also explore a series of planned experiments investigating how users understand and interact with policies in a simplified version of our formalism.

Participating in kindness activities, being generous and showing gratitude, can help people increase their overall happiness levels and improve their levels of wellbeing. This paper describes an installation which will run during the UMAP 2018 Conference. We demonstrate how a gamified digital behavior change intervention can be used to encourage people of different personality types to engage in simple acts of kindness. The system will have implications for future work on personalising persuasive interventions for wellbeing and developing user models through self-assessment and objective behavioural outcomes.

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.

Systems of autonomous and self-interested agents interacting to achieve individual and collective goals may exhibit undesirable or unexpected behaviour if left unconstrained. Norms have been widely proposed as a means of defining and enforcing societal constraints by using the deontic concepts of obligations, permissions and prohibitions to describe what must, may and should not be done, respectively. However, recent efforts to provide norm-enabled agent architectures that guide plan choices suffer from interfering with an agent?s reasoning process, and thus limit the agent?s autonomy more than is required by the norms alone. In this paper we describe an extension of the Beliefs-Desires-Intentions (BDI) architecture that enables normative reasoning used to help agents choose and customise plans taking norms into account. The paper makes three significant contributions: we provide a formal framework to represent norms compactly and to manage them; we present a formal characterisation of the normative positions induced by norms of an agent?s execution within a given time period; and finally, we put forth a mechanism for plan selection and ranking taking into consideration a set of normative restrictions.

The conclusions drawn from a dialogue depend both on the content of the arguments, and the level of trust placed in the arguments and the entity advancing them. In this paper, we describe a framework for dialogue where such trust forms the basis for expressing preferences between arguments, and in turn, for computing conclusions of the dialogue. Our framework contains object and meta-level arguments, and uses ASPIC+ to represent arguments, while argument schemes capture meta-level arguments about trust and preferences.

We present an extension-based approach for computing preferences in an abstract argumentation system. Although numerous argumentation semantics have been developed previously for identifying acceptable sets of arguments from an argumentation framework, there is a lack of justification behind their acceptability based on implicit argument preferences. This paper presents a novel algorithm for exhaustively computing and enumerating all possible sets of preferences for a conflict-free set of arguments in an abstract argumentation framework. We prove the soundness and completeness of the algorithm. The research establishes that preferences are determined using an extension-based approach after the evaluation phase (acceptability of arguments) rather than stated beforehand. We also present some novel insights by comparing the computed preferences for the extensions.

This paper seeks to better understand the links between human reasoning and preferred extensions as found within formal argumentation, especially in the context of uncertainty. The degree of believability of a conclusion may be associated with the number of preferred extensions in which the conclusion is credulously accepted. We are interested in whether people agree with this evaluation. A set of experiments with human participants is presented to investigate the validity of such an association. Our results show that people tend to agree with the outcome of a version of Thimm’s probabilistic semantics in purely qualitative domains as well as in domains in which conclusions express event likelihood. Furthermore, we are able to characterise this behaviour: the heuristics employed by people in understanding preferred extensions are similar to those employed in understanding probabilities. Postprint

In this paper we investigate the links between instantiated argumentation systems and the axioms for non-monotonic reasoning described in [15] with the aim of characterising the nature of argument based reasoning. In doing so, we consider two possible interpretations of the consequence relation, and describe which axioms are met by \({{\textsc {aspic}}^\mathbf {+}}\) under each of these interpretations. We then consider the links between these axioms and the rationality postulates. Our results indicate that argument based reasoning as characterised by \({{\textsc {aspic}}^\mathbf {+}}\) is—according to the axioms of [15]—non-cumulative and non-monotonic, and therefore weaker than the weakest non-monotonic reasoning systems considered in [15]. This weakness underpins \({{\textsc {aspic}}^\mathbf {+}}\)’s success in modelling other reasoning systems. We conclude by considering the relationship between \({{\textsc {aspic}}^\mathbf {+}}\) and other weak logical systems.

Monitoring plan execution is useful in various multi-agent applications, from agent cooperation to norm enforcement. Realistic environments often impose constraints on the capabilities of such monitoring, limiting the amount and coverage of available sensors. In this paper, we consider the problem of sensor placement within an environment to determine whether some behaviour has occurred. Our model is based on the semantics of planning, and we provide a simple formalism for describing sensors and behaviours in such a model. Given the computational complexity of the sensor placement problem, we investigate heuristic techniques for performing sensor placement, demonstrating that such techniques perform well even in complex domains.

Our wider research project investigates the design of a persuasive game for preventing mental health problems and improving subjective wellbeing in a student population. In this paper, we explore how persuasive game elements and interactions can be adapted to different student personalities, active stressors and attitudes. In six focus groups we investigated (1) which key stressors are experienced by students, (2) what characteristics of students need to be considered for adapting game interactions and challenges, and (3) which approaches to personalisation could be applied. Participants were shown stories about a fictional student, conveying high and low levels of three personality traits (Conscientiousness, Emotional Stability and Extraversion), levels of active stressors, and varying attitudes towards change. Participants discussed how to tailor game interactions, activities and challenges to the characteristics of the fictional student. In general, participants perceived real-time personalisation using implicit measures as more effective, but recognised explicit profiling as a valuable complementary method. These findings have implications for the personalisation and design of persuasive game based interventions for health.

Modern multiorganizational coalitions can bring diverse sets of capabilities, assets, and information sources to bear on complex and dynamic operations. However, successfully completing these operations places demands on the trust between coalition partners. When it's necessary to rely on other partners, decision makers must be able to make rapid and effective trust assessments and decisions. Here, the authors focus on coalition information acquisition and discuss mechanisms for assessing trust and arriving at decisions about how to act when trust can be supplemented by controls. They also discuss future directions for these systems and highlight outstanding challenges.

We examine how persuasive messages can be used to promote and encourage healthy eating based on personality. After a personality assessment, participants assessed the persuasiveness of messages designed using Cialdini's principles of persuasion. The results of our study indicate that 'Authority' messages were most influential. In addition, we observed that positively framed messages were significantly more persuasive than negatively framed ones. Furthermore, personality had a significant influence on the best message type, with agreeable subjects being more inclined to persuasion than others.

This paper considers how persuasive messages – within the healthy eating domain – should be communicated to individuals with different personality types. Following a personality assessment, subjects imagined themselves in a scenario and evaluated the effectiveness of messages constructed using Cialdini’s principles of persuasion. Our results suggest that messages exploiting the principle of authority are the most effective across a range of personality types. In addition, personality had a statistically significant impact on the persuasiveness of messages, with “conscientious” subjects more willing to be persuaded than others. Finally, we found that positively framed messages were more preferred than negatively ones. We also found some interaction effects between personality traits and Cialdini’s principles and framing on persuasiveness.

Flexible transport systems (FTS) offer a promising approach to improving the efficiency and performance of passenger transportation services. FTS aim to provide passengers with flexibility in choosing routes, times, modes of transport, service provider and payment systems. In order to achieve this additional flexibility, a well-designed FTS integrate different modes of transport, possibly spanning multiple service providers, to provide more sophisticated, comfortable and cost-effective transport opportunities. The concept of flexible transport is not new; many existing systems, including shared taxicabs, Dial-A-Ride services, and car-clubs, contain elements of such a system. In this paper, we concentrate on FTS within rural areas, which generally suffer from lack of service availability and demand uncertainties, and for which existing FTS solutions are not well suited. We present an agent-based flexible transport systems platform developed using argumentation theory. Formal argumentation is a powerful technique borrowed from artificial intelligence, and in this context is used to weigh-up the conflicting choices available to both passengers and service providers. The resultant platform for FTS in rural areas acts as a virtual transport market place that would more effectively match existing demand and supply for transport services than existing solutions.

In this paper we propose a labelling based dialogue game for determining whether a single argument within a Dung argumentation framework is skeptically preferred. Our game consists of two phases, and determines the membership of a single argument within the extension, assuming optimal play by dialogue participants. In the first phase, one player attempts to advance arguments to construct an extension not containing the argument under consideration, while the second phase verifies that the extension is indeed a preferred one. Correctness within this basic game requires perfect play by both players, and we therefore also introduce an overarching game to overcome this limitation.

This paper introduces a subjective logic based argumentation framework primarily targeted at evidential reasoning. The framework explicitly caters for argument schemes, accrual of arguments, and burden of proof; these concepts appear in many types of argument, and are particularly useful in dialogues revolving around evidential reasoning. The concept of a sensor is also useful in this domain, representing a source of evidence, and is incorporated in our framework. We show how the framework copes with a number of problems that existing frameworks have difficulty dealing with, and how it can be situated within a simple dialogue game. Finally, we examine reasoning machinery that enables an agent to decide what argument to advance with the goal of maximising its utility at the end of a dialogue.

Reasoning about what is best for an agent to do in a particular situation is a challenging task. What makes it even more challenging in a dynamic environment is the existence of norms that aim to regulate a self-interested agent’s behaviour. Practical reasoning is reasoning about what to do in a given situation, particularly in the presence of conflicts between the agent’s practical attitude such as goals, plans and norms. In this paper we: (i) introduce a formal model for normative practical reasoning that allows an agent to plan for multiple and potentially conflicting goals and norms at the same time (ii) identify the best plan(s) for the agent to execute by means of argumentation schemes and critical questions (iii) justify the best plan(s) via an argumentation-based persuasion dialogue for grounded semantics.

This paper describes a framework for practical reasoning in the presence of norms. We describe a formal normative model constructed using Action-based Alternating Transition Systems. This model is able to represent goals; obligations and prohibitions and their violation; and permissions, which are used to derogate the former. Inspired by Atkinson’s scheme for practical reasoning, we utilise argument schemes and critical questions to both show and reason about how goals and obligations lead to preferences over the possible executions of the system. The model then allows us to determine if sufficient information has been provided in order to perform practical reasoning, identify the best courses of action, and explain why specific sequences of actions should be executed by agents within the system.

The exchange of arguments between agents can enable the achievement of otherwise impossible goals, for example through persuading others to act in a certain way. In such a situation, the persuading argument can be seen to have a positive utility. However, arguments can also have a negative utility – uttering the argument could reveal sensitive information, or prevent the information from being used as a bargaining chip in the future. Previous work on arguing with confidential information suggested that a simple tree based search be used to identify which arguments an agent should utter in order to maximise their utility. In this paper, we analyse the problem of which arguments an agent should reveal in more detail. Our framework is constructed on top of a bipolar argument structure, from which we instantiate bonds – subsets of arguments that lead to some specific conclusions. While the general problem of identifying the maximal utility arguments is NP-complete, we give a polynomial time algorithm for identifying the maximum utility bond in situations where bond utilities are additive.

A limitation of standard Description Logics is its inability to reason with uncertain and vague knowledge. Although probabilistic and fuzzy extensions of DLs exist, which provide an explicit representation of uncertainty, they do not provide an explicit means for reasoning about second order uncertainty. Dempster-Shafer theory of evidence (DST) overcomes this weakness and provides means to fuse and reason about uncertain information. In this paper, we combine DL-Lite with DST to allow scalable reasoning over uncertain semantic knowledge bases. Furthermore, our formalism allows for the detection of conflicts between the fused information and domain constraints. Finally, we propose methods to resolve such conflicts through trust revision by exploiting evidence regarding the information sources. The effectiveness of the proposed approaches is shown through simulations under various settings. U.S. Army Research Laboratory ; U.K. Ministry of Defence.

We describe a persuasive dialogue component for promoting and encouraging social interaction for older adults. In order to identify effective persuasive strategies we conducted an interview study with formal carers for older adults. From the results of our study we have identified 6 persuasive strategies, information requirements and developed a model using rules for assessing older adult profiles and applying persuasive strategies.

The fares that passengers are asked to pay for their journey have implications on such things as passenger transport choice, demand, cost recovery and revenue generation for the transport provider. Designing an efficient fare structure is therefore a fundamental problem, which can influence the type of transport options passengers utilise, and may determine whether or not a transport provider makes profit. Fixed pricing mechanisms (e.g., zonal based fares) are rigid and have generally been used to support flexible transport services; however, they do not reflect the cost of provision or quality of service offered. In this paper, we present a novel approach that incorporates variable pricing mechanisms into fare planning for flexible transport services in rural areas. Our model allows intelligent agents to vary the fares that passengers pay for their journeys on the basis of a number of constraints and externalities. We empirically evaluate our approach to demonstrate that variable pricing mechanisms can significantly improve the efficiency of transport systems in general, and rural transport in particular. Furthermore, we show that variable pricing significantly outperforms more rigid fixed price regimes.

Computational trust mechanisms aim to produce a trust rating from both direct and indirect information about agents behaviour. Josang's Subjective Logic has been widely adopted as the core of such systems via its fusion and discount operators. Recently we proposed an operator for discounting opinions based on geometrical properties, and, continuing this line of investigation, this paper describes a new geometry based fusion operator. We evaluate this fusion operator together with our geometric discount operator in the context of a trust system, and show that our operators outperform those originally described by Josang. A core advantage of our work is that these operators can be used without modifying the remainder of the trust and reputation system.

Computational models of argumentation are an active research discipline within Artificial Intelligence that has grown since the beginning of the 1990s (Dung 1995).\ud While still a young field when compared to areas such as SAT solving and Logic Programming, the argumentation community is very active, with a conference series (COMMA, which began in 2006) and a variety of workshops and special issues of journals. Argumentation has also worked its way into a variety of applications. For example, Williams et al. (2015) described how argumentation techniques are used for recommending cancer treatments, while Toniolo et\ud al. (2015) detail how argumentation-based techniques can support critical thinking and collaborative scientific inquiry or intelligence analysis.\ud Many of the problems that argumentation deals with are computationally difficult, and applications utilising argumentation therefore require efficient solvers. To encourage this line of research, we organised the First International\ud Competition on Computational Models of Argumentation (ICCMA), with the intention of assessing and promoting state of the art solvers for abstract argumentation problems, and to identify families of challenging benchmarks for\ud such solvers.\ud The objective of ICCMA’15 is to allow researchers to compare the performance of different solvers systematically on common benchmarks and rules. Moreover, as witnessed by competitions in other AI disciplines such as planning and SAT solving, we see ICCMA as a new pillar of the community which provides information and insights on the current state of the art, and highlights future\ud challenges and developments.\ud This article summarises the first ICCMA held in 2015 (ICCMA’15). In this competition, solvers were invited to address standard decision and enumeration problems of abstract argumentation frameworks (Dunne and Wooldridge 2009).\ud Solvers’ performance is evaluated based on their time taken to provide a correct solution for a problem; incorrect results were discarded. More information about the competition, including complete results and benchmarks, can be found on the ICCMA website.

In modern coalition operations, decision makers must be capable of obtaining and fusing data from diverse sources. The reliability of these sources can vary, and, in order to protect their interests, the data they provide can be obfuscated. The trustworthiness of fused data depends on both the reliability of these sources and their obfuscation strategy. Information consumers must determine how to evaluate trust in the presence of obfuscation, while information providers must determine the appropriate level of obfuscation in order to ensure both that they remain trusted, and do not reveal any private information. In this paper, through a coalition scenario, we discuss and formalise trust and obfuscation in these contexts and the complex relationships between them.

In this paper we extend the traditional Dung argumentation framework with cardinality constraints over the set of warranted arguments. This results in a new definition for argumentation semantics wherein arguments within an extension are both in some sense consistent and compliant with the constraints imposed on the system. After discussing the theoretical aspects of such a resource-bounded argumentation framework we describe its utility via an application to a concrete application domain: the scheduling of demand responsive transport.

In this paper, we extend Dung's seminal argument framework to form a probabilistic argument framework by associating probabilities with arguments and defeats. We then compute the likelihood of some set of arguments appearing within an arbitrary argument framework induced from this probabilistic framework. We show that the complexity of computing this likelihood precisely is exponential in the number of arguments and defeats, and thus describe an approximate approach to computing these likelihoods based on Monte-Carlo simulation. Evaluating the latter approach against the exact approach shows significant computational savings. Our probabilistic argument framework is applicable to a number of real world problems; we show its utility by applying it to the problem of coalition formation.

Norm-aware agents are able to reason about the obligations, permissions and prohibitions that affect their operation. While much work has focused on the creation of such norm-aware agents, less effort has been placed on enabling system designers and users to understand the interactions between norms. Providing designers with such an understanding can aid in eliminating redundant norms and errors in norm specifications, while enhancing user understanding can increase the trust placed in a system. In this paper we make use of conceptual graph based semantics to provide a graphical representation that is designed to enhance the understanding of the interactions between different types of norms. More specifically, permissions derogate obligations and prohibitions, and the latter two norm types interact by conflicting with each other. Tracking these interactions in standard symbolic norm representations is difficult given a large set of norms, yet our work allows for the easy understanding of whether a permission causes obligation or prohibition derogation.

The behaviour of deliberative agents is often guided by a plan library designed to achieve goals given certain environmental conditions. Plans in these plan libraries are designed to achieve individual goals, and cannot possibly account for all possible variations of restrictions in the societies within which these agents must operate. These restrictions, captured through norms, include obligations, prohibitions, and permissions. Unlike traditional planning restrictions, norms can often be contradictory and impossible to achieve simultaneously, necessitating some form of compromise. In this paper we describe a technique for taking norms into consideration when deciding how to execute a plan. Our norms are constraint based, allowing for fine-grained control over actions. Our technique allows for reasoning about the interactions between norms, and resolves conflict by selecting actions where the cost of violating one set of norms is outweighed by the reward obtained in complying with another.

Pur P ose . To characterize short-term changes induced by pars plana vitrectomy on anterior segment depth and lens thickness by means of high-frequency ultrasound biomicroscopy. Methods . We undertook a prospective case series study on consecutive patients referred for pars plana vitrectomy as the sole procedure in our institution between 2005 and 2007. Twenty-eight eyes of 28 patients undergoing pars plana vitrectomy were studied by ultrasound biomicroscopy. Neither silicone oil nor scleral buckle was used for any of the procedures. Anterior chamber depth and lens thickness were compared before and immediately after surgery, and their results were compared to the postoperative intraocular pressure measurements. results . There was a significant decrease in anterior chamber depth in the study eyes of patients who had undergone gas tamponade (p

While researchers have looked at many aspects of argumentation, an area often neglected is that of argumentation strategies. That is, given multiple possible arguments that an agent can put forth, which should be selected in what circumstances. In this paper we propose a heuristic that implements one such strategy. The heuristic is built around opponent modelling, and operates by selecting the line of argument that yields maximal utility, based on the opponent's expected response, as computed by the opponent model. An opponent model may be recursive, with the opponent modelling of the agent captured by the original agent's opponent model. Computing the utility for each possible line of argument is thus done using a variant of M* search, which in itself is an enhancement of min-max search. After describing the M* algorithm we show how it may be adapted to the argumentation domain, and then study what enhancements are possible for more specific types of dialogue. Finally, we discuss how this heuristic may be extended in future work, and its relevance to argumentation theory in general.