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We present a working model of agent’s social behaviour based on drives and values. Drives represent internal needs of the agents; values are used to prioritise them. Discussed in the context of a simulation scenario centred around smoking behaviour in public places, the designed system proves itself useful to tackle issues where agents face conflicting decision’s choices, or where agent’s behaviour has negative side-effects for other agents.

Norms play an important role in coordinating, regulating and predicting agents’ behavior in open multi-agent societies. Much work has been done on modeling and developing normative multi-agent systems. Norms in open multi-agent societies are not fixed, they might emerge, change or vanish; therefore agents need a mechanism to adapt their behavior accordingly. Using Event Calculus we propose a formal representation of prohibition and obligation norms. This includes the norm’s context, rewards and sanctions. Using this formalization we propose a technique for BDI agents to reason at run time about their behavior taking into consideration current norms and past actions performed by the agent. In this work, we assume that the best behavior of an agent is the behavior with maximum utility. Our technique has been applied to a simple mining simulation.

Human users planning for multiple objectives in coalition environments are subjected to high levels of cognitive workload, which can severely impair the quality of the plans created. The cognitive workload is significantly increased when a user must not only cope with a complex environment, but also with a set of unaccustomed rules that prescribe how the coalition planning process must be carried out. In this context, we develop a prognostic assistant agent that takes a proactive stance in assisting cognitively overloaded users by providing timely support for normative reasoning---reasoning about prohibitions and obligations.

Social norms have become one of the most promising approaches that use an informal social control to ensure a desirable social order in open multi-agent systems. In these systems, autonomous and heterogeneous entities work towards similar or different goals. Norms regulate the behaviour of agents by defining obligations and prohibitions, and by creating rewards and penalties to encourage the agents to meet theses constraints. The development of autonomous normative agents, i. e., agents able to take decisions by following their motivations related to their goals and taking into account the system norms, has proven to be significantly more challenging than the design of traditional agents. In this paper, we introduce an architectural model that provides a set of functions to support the agent on the reasoning about the norms. These operations assist the agent to perceive the system's norms, detect the fulfillment and violation of the norms while verifying their activation and deactivation, select the norms the agent intends to fulfill, identify and overcome conflicts among norms.

The values shared within a society influence the (social) behaviour of the agents in that society. This connection goes through implicit and explicit norms. Agents act in situations where different, possibly conflicting, norms are applicable. In the case of a norm conflict, an agent will decide to comply with one or more of the applicable norms, while violating others. Our interest is how the type of the norms may play a role in such decision, and take the chosen behaviour of an agent to depend on a personal preference order on the norm types.We distinguish three different types of norms: legal norms, social norms and private norms. We use the introduction of the law prohibiting smoking in cafes as illustration: we present a simulation of this situation involving agents' preferences over different norm types. The results of this simulation are used for an explorative a model for normative reasoning based on norm types. We discuss a possible connection between the composition of a society in terms of these profiles and its culture and the relevance of the model with respect to value sensitive design of socio-technological systems.

Norm change is one of the main challenges for normative reasoning. This discussion paper presents some novel informal and semi-formal ideas regarding the nature and formalization of norm change. First, we contrast the second-order nature of norm change with the first-order nature of obligation change. Second, we discuss how to change the normative system to avoid future obligations in the input/output logic framework, which explicitly distinguishes norms from obligations. Third, we present a semantical two dimensional view on change. Fourth, we illustrate norm change using automata changing automata. Fifth, we compare norm change with analogous mechanisms in non-normative contexts.

Marek Sergot's technical contributions range over different subjects. He has developed a series of novel ideas and formal methods bridging different research domains, such as artificial intelligence, computational logic, philosophical logic, legal theory, artificial intelligence and law, multi-agent systems and bioinformatics. By combining his background in logic and computing with his interest in the law, deontic logic, action, and related areas, and applying to all his capacity to understand the subtleties of social interactions and normative reasoning, Marek has been able to open new directions of research, and has been a reference, an inspiration, and a model for many researchers in the many fields in which he has worked.

The great majority of works on norm implementation are aimed at enforcing a set of stable norms whose violation can be perfectly observed. However, this assumption is not true in open systems in which norms should evolve for dealing with unforeseen situations. As a solution to this need, we propose to develop norm enforcer agents endowed with capabilities for acquiring norms dynamically and enforcing them in uncertain environments. Specifically, a Normative BDI architecture has been extended in this paper.

Norms have become one of the most promising mechanisms of social control to ensure a desirable social order in open multi-agent systems where autonomous, heterogeneous and independently designed entities can work towards similar or different ends. Norms regulate the behaviour of agents by defining permissions, obligations and prohibitions, and by stating stimulus to their fulfilment while defining rewards and discouraging their violation while pointing out punishments. Since goal-oriented agents' priority is the satisfaction of their own desires, they must evaluate the positive and negative effects of the fulfilment or violation of the norms before choosing to comply or not with them. In this context, we present the new functions of the Jason platform defined to support normative reasoning, i.e, to build agents able to deal with desires and norms. Agents are then able to check if they should adopt or not the norm, evaluate the effects of the fulfilment or violation of the norm on their desires, detect and solve conflicts among norms, and select desires and plans according to their choices of fulfilling or not a norm. We demonstrate the applicability of such new functions through a non-combatant evacuation scenario.

Norms have been employed as a coordination mechanism for Open MAS, but to become effective, they must be internalized by agents; i.e. these agents must be able to accept norms while maintaining their autonomy. Nevertheless, traditional BDI agent architectures only represent beliefs, intentions and desires. In this paper, the multicontext BDI agent architecture has been extended with a recognition context and a normative context in order to allow agents to acquire norms from their environment and consider norms in their decisions.

Rational self-interested agents, which act so as to achieve the best expected outcome, should violate the norms if the expected rewards obtained with the defections from the norms surpass the expected rewards obtained by being norm-compliant. It means they should estimate the earnings brought about by the violations and the losses caused by their respective reactions. In this paper, we present a rational self-interested agent model that takes into account the possibility of breaking norms. To develop such model, we employ Markov Decision Processes (MDPs). Our approach consists of representing the reactions for norm violations within the MDPs in such a way that the agent is able to reason about how those violations affect her expected utilities and future options. Finally, we perform an experiment in order to establish comparisons between the model presented in this work and its norm-compliant version.

We show how protocols (or interaction patterns) can be derived from norms using landmarks. The resulting protocols can be used by agents to perform their interactions while being certain to stay within the norms governing an e-institution without having to have a capability for normative reasoning. It can also be used by normative agents as a default protocol to be used, but from which they can deviate in specific circumstances.

The problem of deriving implicit norms from explicitly given ones is at the heart of normative reasoning. In abstractothe problem is that of formalizing a plausible consequence relation taking norms to norms. I argue that any such relation should allow norms to be chained, even when the consequent of one is strictly stronger than the antecedent of another--i. e. even if logical inference is required to complete the chain. However, since it is commonly agreed that the set of items classically entailed by an obligatory proposition are not in general obligatory, we are left with the following problem: How do reserve the right to reason classically for the purpose of chaining, whilst not committing to the view that all items entailed by a norm are obligatory in the same sense. I shall argue that the problem can be given a natural solution with reference to different usesof a norm in a normative system.

In this paper we use recursive modelling to formalize sanction-based obligations in a qualitative game theory. In particular, we formalize an agent who attributes mental attitudes such as goals and desires to the normative system which creates and enforces its obligations. The wishes (goals, desires) of the normative system are the commands (obligations) of the agent. Since the agent is able to reason about the normative system’s behavior, our model accounts for many ways in which an agent can violate a norm believing that it will not be sanctioned. We thus propose a cognitive theory of normative reasoning which can be applied in theories requiring dynamic trust to understand when it is necessary to revise it.

A theory of diagnosis and qualitative decision theory are able to formalize reasoning with norms. They are thus different from deontic logic, that formalizes reasoning about norms. In this paper, we compare two theories of diagnosis for normative systems: Ramos and Fiadeiro’s theory of diagnosis developed for organizational process design and Tan and Van der Torre’s theory of diagnosis extended with notions of qualitative decision theory. We observe several similarities.

In this paper we discuss the role of context and independence in normative reasoning. First, deontic operators - obligations, prohibitions, permissions - referring to the ideal context may conflict with operators referring to a violation (or contrary-to-duty) context. Second, deontic independence is a powerful concept to derive deontic operators from such operators of other violation contexts. These two concepts are used to determine how to proceed once a norm has been violated, a key issue of deontic logic applications in computer science. We also show how violation contexts and deontic independence can be used to give a new analysis of several notorious paradoxes of deontic logic.

Usually a default rule A : B/C is intended to mean that if A holds in a state of affairs a B is consistent, then C follows by default. However, C is not a necessary conclusion: different states of affairs are possible (conceivable). According to this view, Meyer and van der Hoek developed a multimodal logic, called S5P(n), for treating non-monotonic reasoning in a monotonic setting. In this paper we shall describe a proof search algorithm for S5P(n) which has been implemented as a Prolog Interpreter.