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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.

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.

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 $\sqrt {n\log n}$ 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.

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.

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.

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.

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.

To resolve conflicts among norms, various nonmonotonic formalisms can be used to perform prioritized normative reasoning. Meanwhile, formal argumentation provides a way to represent nonmonotonic logics. In this paper, we propose a representation of prioritized normative reasoning by argumentation. Using hierarchical abstract normative systems, we define three kinds of prioritized normative reasoning approaches, called Greedy, Reduction, and Optimization. Then, after formulating an argumentation theory for a hierarchical abstract normative system, we show that for a totally ordered hierarchical abstract normative system, 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., Accepted by the Journal of Logic and Computation on November 2nd, 2017

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.

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.

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.

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.

Computational trust mechanisms aim to produce trust ratings from both direct and indirect information about agents' behaviour. Subjective Logic (SL) has been widely adopted as the core of such systems via its fusion and discount operators. In recent research we revisited the semantics of these operators to explore an alternative, geometric interpretation. In this paper we present principled desiderata for discounting and fusion operators in SL. Building upon this we present operators that satisfy these desirable properties, including a family of discount operators. We then show, through a rigorous empirical study, that specific, geometrically interpreted, operators significantly outperform standard SL operators in estimating ground truth. These novel operators offer real advantages for computational models of trust and reputation, in which they may be employed without modifying other aspects of an existing system.

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.

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.

International audience; The contract project was a European Commission project whose aim was to develop frameworks,components and tools to model, build, verify and monitor distributed electronic businesssystems based on electronic contracts. In this context, an electronic contract provides a specification of the expected behaviours of individual services, with the assumption that these services are often enacted by autonomous agents. Using the theoretical tools created by the project, in this paper we describe the complete life cycle of instantiating an electronic contracting system using the contract framework within the aerospace aftercare domain. Thus, we use a natural language description of parts of the types of contracts used in this domain to generate individual norms amenable to a computational representation, and how these norms are used to generate a concrete contract monitor. Moreover, we describe a concrete implementation of contract agentsin the AgentSpeak(L) language and how these agents interact within a concrete instantiation of contract.

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.

Research and Development in Intelligent Systems XXVII Incorporating Applications and Innovations in Intelligent Systems XVIII; International audience; Norms impose obligations, permissions and prohibitions on individual agents operating as part of an organisation. Typically, the purpose of such norms is to ensure that an organisation acts in some socially (or mutually) beneficial manner, possibly at the expense of individual agent utility. In this context, agents are normaware if they are able to reason about which norms are applicable to them, and to decide whether to comply with or ignore them. While much work has focused on the creation of norm-aware agents, much less has been concerned with aiding system designers in understanding the effects of norms on a system. The ability to understand such norm effects can aid the designer in avoiding incorrect norm specification, eliminating redundant norms and reducing normative conflict. In this paper, we address the problem of norm understanding by providing explanations as to why a norm is applicable, violated, or in some other state. We make use of conceptual graph based semantics to provide a graphical representation of the norms within a system. Given knowledge of the current and historical state of the system, such a representation allows for explanation of the state of norms, showing for example why they may have been activated or violated.

Electronic contracts mirror the paper versions exchanged between businesses today, and offer the possibility of dynamic, automatic creation and enforcement of restrictions and compulsions on service behaviour that are designed to ensure business objectives are met. Where there are many contracts within a particular application, it can be difficult to determine whether the system can reliably fulfil them all, yet computer-parsable electronic contracts may allow such verification to be automated. In this chapter, we describe a conceptual framework and architecture specification in which normative business contracts can be electronically represented, verified, established, renewed, and so on. In particular, we aim to allow systems containing multiple contracts to be checked for conflicts and violations of business objectives. We illustrate the framework and architecture with an aerospace aftermarket example.

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.

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 two related heuristics that allow an agent to select what utterances to make. The first involves minimising the amount of information revealed in the course of a dialogue. The second heuristic assigns a utility cost to revealing information, as well as a utility to winning, drawing and losing an argument. An agent participating in a dialogue then attempts to maximise its utility. We present a formal argumentation framework in which these heuristics may operate, and show how they function within the framework. Finally, we discuss extensions to the heuristics, and their relevance to argumentation theory in general.

In our view, customers in the future are likely to obtain their services from coalitions of service providers. These coalitions can be described as virtual organisations (VOs); they are group of service providers that form relationships to service customers’ demands on an ad-hoc basis. For a VO to be effective, it must be reliable and scalable, and realistically, it must be created and maintained in a dynamic, open and competitive environment. The CONOISE-G project has focused on resolving the technology challenges that emerged from these requirements. Specifically, CONOISE-G provides mechanisms to assure effective operation of VOs in the face of failure, unexpected events and changing requirements in dynamic, open and competitive environment. In this paper, we describe the CONOISE-G system; motivated by a scenario based on mobile service provision; outline its use in the context of VO formation and perturbation and review current efforts to progress the work to deal with unreliable information sources.

Expressing contractual agreements electronically potentially allows agents to automatically perform functions surrounding contract use: establish- ment, fulfilment, renegotiation etc. For such automation to be used for real busi- ness concerns, there needs to be a high level of trust in the agent-based system. While there has been much research on simulating trust between agents, there are areas where such trust is harder to establish. In particular, contract proposals may come from parties that an agent has had no prior interaction with and, in competitive business-to-business environments, little reputation information may be available. In human practice, trust in a proposed contract is determined in part from the content of the proposal itself, and the similarity of the content to that of prior contracts, executed to varying degrees of success. In this paper, we argue that such analysis is also appropriate in automated systems, and to provide it we need systems to record salient details of prior contract use and algorithms for as- sessing proposals on their content.We use provenance technology to provide the former and detail algorithms for measuring contract success and similarity for the latter, applying them to an aerospace case study.

Clauses within contracts may be thought of as norms, specifying permissions, obligations and prohibitions on contract parties. In this paper, we present a formal representation of contracts, focusing on the specification of a model of norms. With this model, a norm is associated with a status, which may change as the environment, and the status of other norms, changes. We define a normative environment, which may be used to track the status of a set of norms throughout their lifecycle, and then describe a predicates that may be used to evaluate a norm’s status. Agents are able to use these predicates to reason about the status of norms, and how their actions will affect the normative environment. Finally, we show the applicability of our framework to real world domains by monitoring the execution of a contract taken from a real world scenario.

International audience; Last years have seen the raise of several contexts such as Ambient Intelligence, Augmented Reality where Artificial Intelligence is combined with other domains such as Ubiquitous Computing, Sensor Network Technologies in order to provide proactive and responsive services to users. However, these systems are most of the times ad hoc, lacking a conceptual foundation. In this paper we provide a broad overview of mirror worlds, as physically situated agent societies, useful in particular as a framework for investigating inter-disciplinary aspects – from cognition to interaction, cooperation, governance – concerning future smart environments and cities shaped as large-scale mixed-reality systems.