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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 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.

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.

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.

The behaviours of autonomous agents may deviate from those deemed to be for the good of the societal systems of which they are a part. Norms have therefore been proposed as a means to regulate agent behaviours in open and dynamic systems, and may be encoded in electronic contracts in order to specify the obliged, permitted and prohibited behaviours of agents that are signatories to such contracts. Enactment and management of electronic contracts thus enables the use of regulatory mechanisms to ensure that agent behaviours comply with the encoded norms. To facilitate such mechanisms requires monitoring in order to detect and explain violation of norms. In this paper we propose a framework for monitoring that is to be implemented and integrated into a suite of contract enactment and management tools. The framework adopts a non-intrusive approach to monitoring, whereby the states of a contract with respect to its contained norms can be inferred on the basis of messages exchanged. Specifically, the framework deploys agents that observe messages sent between contract signatories, where these messages correspond to agent behaviours and therefore indicate whether norms are, or are in danger of, being violated.

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.

Agents may choose to ignore contract violations if the costs of enforcing the contract exceed the compensation they would receive. In this paper we provide an argumentation based framework for agents to both decide whether to enforce a contract, and to undertake contract enforcement actions. The framework centers around the agent reasoning about what arguments to put forth based on a comparison between the utility it would gain for proving its case and the utility it loses for probing environment state.