CRITERIOS CONTABLES APLICADOS

As we explained in Section 2.2.2.3.1agents may exist within regulated environments where norms might be dynamically changing over time and, at the same time, they may be simultaneously operating in multiple normative contexts. In order to moni- tor the correct execution of the norms imposed, complex mechanisms which are able to interpret institutional facts as well as perceiving the normative status of the en- vironment are needed. Such mechanisms that can interpret and follow the different phases that norms go through, from their generation, throughout their enforcement and till they get fulfilled, expired or even withdrawn, have been thoroughly examined in the literature. Although norm monitoring is out of the scope of this thesis, in this section we describe some important frameworks that cover the monitoring aspect of normative systems.

Artikis et al. view view societies as instances of normative systems [Artikis,2003;Ar- tikis et al.,2003,2009] and in [Artikis,2003;Artikis et al.,2003] they describe normative social systems in terms of power, empowerment and obligation and create operational specifications using both event calculus [Kowalski and Sergot, 1986] and the action language C+ [Giunchiglia et al.,2004], demonstrating how they can be implemented via existing tools. In his doctoral thesis Artikis [Artikis, 2003] gives examples of how roles, obligations, permissions and institutionalised power can be formally expressed

and uses a Contract Net protocol [Smith, 1980] to demonstrate an how the specifica- tion can be analysed and queried via an implementation in the CCalc tool [McCain, 1997]. Later, in [Artikis and Sergot,2010] Artikis and Sergot use event calculus to for- mulate obligations, permissions and power and track normative states of multi-agent systems. The normative statements refer to actions rather than states to be brought about. Violations are also specified but the authors associate no specific consequences to them.

[Farrell et al., 2005] described a predicate-based event calculus approach for keeping track of normative state in contracts. Their work focuses on creating an XML-based representation of event calculus, and uses event calculus primitives to define their con- tracts. This however results in a very unmanageable and impracticable contract repre- sentation with a small number of norm related predicates. In addition, [Daskalopulu, 2000] showed how contract monitoring can be performed with Petri nets. However, this representation is more appropriate for contracts which can be modelled as work- flows.

In [Cliffe, 2007; Cliffe et al., 2007b,a] the authors extend the formal specification of single institutions to multi-institutions. They present a top-down approach to vir- tual multi-institutions in which agents may reason (on-line) about and designers may analyse (off-line) external normative concepts. They introduce an action language de- signed for multi-institutions. The action language describes a model of the situation which can be directly mapped to an answer set program (ASP) allowing for an easy way to query properties of models. They define institutional state as a set of institu- tional fluents that may be held to be true at some instant. Furthermore, they separate such fluents into domain fluents, that depend on the institution being modelled and normative fluents. They define generation functions of events which have effects on the system and using the normative fluents they track the status of the norms at each state and detect violations.

In [Modgil et al., 2009] Modgil et al. propose an architecture for monitoring norm- governed systems. The system deploys monitors that take inputs from trusted ob- servers, and processes them together with Augmented Transition Network (ATN) rep- resentations of individual norms. Hence, monitors determine the states of interest relevant to the activation, fulfilment, violation and expiration of norms. Additionally, the monitoring system is corrective in the sense that it allows norm violations to be detected and be reacted to.

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Alvarez-Napagao suggests in [Alvarez-Napagao et al.,´ 2010] the use of production systems [Davis and King, 1975] to create a normative monitor. Regulative norms are defined through activation, maintenance, deactivation, deadline conditions. The authors also include counts-as norms. The norms status can be inactive, activated, fulfilled or violated. Using a forward-chaining rule engine, they create inference rules which, by checking past events against active norms, calculate each norm’s status. Norms might have several instances and the authors design the rules in a way so that

multiple instances can be handled. The formalism is then reduced to a production sys- tem (a system composed of rules, a working memory and a rule-processing engine) [Davis and King, 1975] so that it operates as a practical monitor which detects viola- tions and enforces sanctions. The authors also provide an implementation made with theDROOLSrule engine [The JBoss Drools team, 2013]. G ´omez-Sebastia et al. later ex-

tended the same monitoring framework to handle update of normative contexts (norm additions or deletions) at runtime, without needing to end the monitoring execution [G ´omez-Sebastia et al.,2012]. They provided different updating processes that might take place both while taking or without taking past events into consideration.

Cranefield et al. [Cranefield and Winikoff, 2011;Cranefield et al., 2012], on the other hand, do not differentiate between norms and commitments, they instead use a generic term and study the general notion of expectations for future world states, events and actions. They use the Exp, Fulf and Viol operators, all of which have similar argument structure, to express a currently active, fulfilled or violated expectation. A formula Exp(λ,ρ,n,φ)signifies that φ is an active expectation as a result of a rule of the form

λV ρ having fired in a (possibly prior) state specified by n. The authors also use for-

mula progression14to show how an unfulfilled and non-violated expectation is updated from one state to the next. The approach is claimed by the authors to apply to both online and offline monitoring of rule-based conditional expectations, and their fulfill- ments and violations, through a model checker extended with the ability to progress expectation expressions.

Finally, Hindriks and van Riemsdijk suggest a labelling mechanism to track down the norms’ status in timed transition systems, that is, transition systems extended with time [Hindriks and van Riemsdijk, 2013]. In their framework, norms are defined as tuples containing an activation condition, the normative target to achieve and a time before which it has to be achieved. They define detached obligations as obligations brought to life whenever a norm becomes applicable. On top of this, they introduce two binary relations, a blocking relation B where a norm can block the application of a norm appearing later and a cancelling relation C where a norm can cancel another norm that occurred earlier. The labelling process labels the transition system’s states in two ways: 1) indicating at every state which obligations hold and 2) indicating at every state the violations that have possibly occurred. The authors then define the detachment, persistence (the continuation of activeness of a norm through time unless otherwise specified), termination and violation of norms with respect to the blocking and cancelling relations and the labels applied to the transition system. There exists no available implementation of this approach.