BDI-based Normative Autonomous Agents

The BDI model (see Section2.1.1.1) is by many now considered the standard approach to agent architectures, as it provides a wide span of behaviours from solely deliberative to simply reactive, depending on the agent cycle and plans’ implementation. Given the agent’s autonomous nature, several researchers have made important attempts to incorporate social influences into BDI agents, expressed not as rigid constraints, but instead, as norms, aiming for an even larger spectrum of behaviours to be represented in one sole framework.

Dignum et al. in [Dignum et al.,2000] propose a modification of the BDI architecture into a socially motivated deliberation process, taking the influence of social obligations and norms on the deliberation process into account. The approach focuses on the pro- cess of generating (candidate) intentions from normative influences. The authors use the notion of both norms and obligations. Norms have a social aspect and make co- operation and coordination and interaction more efficient. Obligations on the other hand are associated with specific enforcement strategies which involve punishment of violators and in this way they restrict autonomy. For norms, the preference ordering is related to the “social benefit” attached to different worlds and for obligations, the preference ordering is related to “penalties” imposed for violation. The basic deliber- ation cycle, which includes a process of events selection and plan generation through the selected events, is modified by involving the notion of deontic events and potential deontic events. The former are generated from changes in the norms, obligations, and beliefs of an agent. To respond to these deontic events, each agent has plans whose invocation conditions are deontic events. The latter are events that may also exist, depending upon what plan-options (decided by the option-generator) are decided in the deliberation step. An additional step is then introduced to the agent’s loop is as fol- lows: some set of events is chosen and is augmented with potential deontic events that are generated by repeatedly applying the introspective norms and obligations. This augmented set of events is the one that will be used to determine the plan-options

calculated. The deliberation step then selects between these sets of plans on the basis of the preferences. There is no implementation available for this work.

Broersen et al. [Broersen et al., 2001] present the BOID (Belief-Obligation-Intention- Desire) architecture as a model of a norm-governed agent. It contains four components (B, O, I and D) where B stands for beliefs, O stands for obligations (representing commitments towards social rationality), I stands for intentions and D for desires. The behaviour of each component is defined by formulas. More specifically, extensions are propositional logical formulas defining each component’s behaviour in the form of defeasible rules. An ordering function on rules is used to resolve conflicts between components. The authors propose a calculation scheme to build in each cycle the new set of logical formulas. Then, in order to produce the whole extension of the agent every time, the process starts with the observations and calculates (through the calculation scheme) a belief extension and then, when done, applicable rules from O, I and D are applied successively, each time feeding back the belief component for reconsideration. The order in which components are chosen for rule selection determines the kind of character the agent possesses. For example, if obligations are considered before desires, the agent is regarded as a social agent. One drawback is that the creators only consider extensions in which the belief component overrules any other modality. Furthermore, the ordering function is fixed for each agent.

In [Meneguzzi and Luck, 2009b] the authors extend a BDI agent language, enabling the agents to enact behaviour modification at runtime in response to newly accepted norms. According to their specification, a norm (obligation or prohibition) can refer to a state or an action and has a validity period defined by an activation and an expiration condition. An agent might accept or reject a norm (a process not dealt with inside the framework). The authors provide methodologies to react to norms’ activation and norm’s compliance. These consist of forming new plans (inserting them to the plan library) to comply with obligations and preventing existing plans (deleting them from the plan library) that violate prohibitions from being carried out. They demonstrate their framework’s practical usefulness via an implementation in AgentSpeak(L) [Rao, 1996].

Another interesting piece of research on normative BDI agents is [Criado et al.,2010a,b]. They base their work on graded BDI agents. According to the graded BDI architecture (n-BDI for short) [Casali,2008], an agent is defined by a set of interconnected contexts (mental, functional and normative contexts), where each of them has its own logic (i.e. its own language, axioms and inference rules). In addition, bridge rules, whose premises and conclusions are in different contexts, are inference rules derived by one context and modifying the theory of another. In [Criado et al., 2010a,b] the authors propose an extension of the n-BDI architecture, in order to help agents to take practical autonomous decisions with respect to the existence of norms. They use rules to de- cide on norm adoption as desires. They apply deliberative coherence and consistency theory for determining which norms are more coherent with respect to the agent’s mental state. The authors suggest a methodology to detect and resolve inconsistencies

between norms and desires. A basic difference in their approach, however, is that it mainly focuses on the reasoning over the adoption (or not) of instantiations of norms rather than suggesting whether and by what means to achieve the norms’ fulfilment. Moreover, they assume: 1) a quantification of the mental context (beliefs, desires, in- tentions) associated to the certainty degree of each of these elements; 2) punishing and rewarding reactions for each norm’s violation or fulfilment; 3) predefined func- tions that will determine the adoption or not of a norm instantiation; 4) predefined values for the weights expressing the strength of the mental context elements that are related to a norm. We find that it can be difficult to estimate or predetermine all these factors when designing norms. One more limitation is that the approach only considers obligation norms.

In [Ranathunga et al., 2012] Ranathunga et al. try to see norm monitoring from the individual agent perspective rather than from the standard organisational perspective and integrate their previous theoretical work [Cranefield and Winikoff,2011] on expec- tation monitoring (explained in Section2.2.2.3.5) into the Jason [Bordini and H ¨ubner, 2006] platform. They extend the Jason interpreter by adding two internal actions that represent the initiation and termination mechanism for the monitor to its standard actions library. They define the extended Jason configuration to be a combinatory configuration of the Jason agent and the monitor. Whenever the monitor detects fulfil- ment of violation of a rule it notifies the agent, which in its turn can react accordingly by executing predefined plans triggered by such events. The authors explain how a current limitation of the system only permits the handling of only one rule at a time. In [Alechina et al.,2012] the authors use a norm formalism for obligations and prohi- bitions that contains pre-specified sanctions in case of violation. Based on the 2APL agent programming language, they extend its PG-rules reasoning rules (rules that se- lect pre-defined plans to be executed) to contain event-based rules that initiate norms (obligations and prohibitions) and name it N-2APL. Whenever these event-based rules are triggered, obligations are adopted as goals and prohibitions are activated. By defining a priority ordering function that indicates the agent’s preferences over goals as well as sanctions for violating obligations and prohibitions, they design an algo- rithm to calculate the set of plans that will be optimal with respect to this function. An agent might have a “social” character if its obligations are preferred to its goals, trying to primarily fulfil these obligations. On the other hand, if an agent gives pri- ority over its goals, then it might end up breaching a lot of norms and getting highly sanctioned for this.

Taking Alechina’s work as basis, in [Dybalova et al., 2013] the authors et al. make an integration of N-2APL with the organisation programming language 2OPL [Dastani et al.,2009a] in order to create a system where, instead of the norms being an internal part of the agent, they are imposed exogenously, that is, by a normative organisation. The communication between the 2APL agents and the 2OPL normative organisation is done through a tuple space. These tuples are accessed just like an external environ- ment and they represent the state of the multi-agent system and its normative state

in terms of active obligations, prohibitions and applicable sanctions. As we explain again later in Section 3.3.2, the methodology uses the plan library (pre-stated plan- ning rules) of the agent and does not explore a dynamic planning mechanism for the creation of new plans.