Providing Contextual Norm Information in Open Multi-Agent Systems Carolina Felicíssimo1, 3, Ricardo Choren2, Jean-Pierre Briot1, 3, Carlos Lucena1, Caroline Chopinaud3 and Amal El Fallah Seghrouchni3 1
DI, PUC-Rio: Rua M. de São Vicente, 225, Gávea Rio de Janeiro, RJ, 22453-900, Brasil {cfelicissimo,lucena}@inf.puc-rio.br 2 SE-8, IME: Pca General Tiburcio 80, 22290-270, Rio de Janeiro RJ, Brazil
[email protected] 3 LIP6, Paris VI: 104 Avenue Kennedy, 75016, Paris, France {jean-pierre.briot,caroline.chopinaud,amal.elfallah}@lip6.fr
Abstract. Agents can freely migrate among open MASs in order to obtain resources or services not found locally. In this scenario, agent actions should be guided for avoiding unexpected behavior. However, open MASs are extremely dynamic and, thus, a solution for guiding agent actions is non-trivial. This work details DynaCROM, our solution for continuously supporting agents in open MASs with updated norm information. The main asset of DynaCROM is that it decreases the complexity of norm representation by using contexts. DynaCROM proposes (i) a top-down modeling of contextual norms, (ii) an ontology to explicitly represent norm semantics and (iii) a rule inference engine to customize different compositions of contextual norms. Thus, DynaCROM offers a solution for both developers to enhance their open MASs with norm information and agents to be continuously supported with precise norm information.
1 Introduction Multi-agent systems (MASs) have emerged as a promising approach to develop information systems that clearly require several goal-oriented problem-solving entities [21]. Following this direction, we believe that near information systems will be implemented as open MASs, which will be composed of many sets of heterogeneous self-interested agents. These agents will be mobile agents, i.e. they will have the capability to freely migrate among MASs for obtaining resources or services not found locally. An MAS can be considered an open system when it presents the following characteristics [13]: Heterogeneity: agents are possibly developed by different parties, in different programming languages, with different purposes and preferences. Accountability: agent actions must be monitored to detect the execution of behaviors that may not be according to the overall expected functioning of the system. Social change: agent societies are not static; they may evolve over time by updating their information. So, future changes should be easily accommodated.
In open MASs, agents may be heterogeneous, but they all must know how to assimilate provided information for effective execution. In this sense, information should be expressed in a meaningful way for agents, avoiding misunderstandings. Moreover, the intrinsic dynamics of open MASs should be supported by a flexible mechanism that easily permits data updates. Regarding these points, we developed a solution for continuously providing contextual norm information to agents in open MASs. In our opinion, open MASs should be enhanced with norm information for guiding agent actions. Our solution, called DynaCROM (meaning dynamic contextual regulation information provision in open MASs)[8-11], proposes (i) a top-down modeling of contextual norms, (ii) an ontology to explicitly represent norm semantics and (iii) a rule inference engine to customize different compositions of contextual norms. DynaCROM allows for accountability since MASs and their agents continuously have information about which norms they should follow. DynaCROM also allows for social changes since information is defined in a central resource (an ontology) that can easily have its data composed (thanks to a rule support) and updated. It is important to stress here that, in this work, we make no assumptions about whether agents decide or not to be compliant with norms. DynaCROM allows the modeling and representation of customized compositions of contextual norms, offering precise norm information for agents to consider in a given context. Thus, DynaCROM provides the way for agents to reason about norm compliance and for developers to implement normative MASs. Norm-aware agents are more likely to perform correctly and, consequently, to achieve their goals faster. The remainder of this paper is organized as follows. Section 2 details DynaCROM, including its top-down modeling, use of ontology and rules, and implementation. Section 3 describes a case study. Section 4 briefly presents how DynaCROM answers (contextual norm information) can be used. Section 5 compares DynaCROM with two related works. Finally, Section 6 concludes the paper and outlines future works.
2 Contextual Norm Information Provision in Open MASs MASs are generally made up of environments, organizations and agents [20]. Environments [36] are discrete computational locations (similar to places in the physical world) that provide conditions for agents to inhabit it. Environments can have refinement levels, such as a specialization relationship (e.g., country, state, etc.), but there cannot be overlaps (e.g., there cannot be two countries in the same place). An environment also can have many organizations. Organizations [12] are social locations inside which groups of agents play roles. Furthermore, an organization can have many sub-organizations, but each organization belongs to only one environment [28]. An agent can be in different organizations; and, agents with the mobility characteristic can migrate among environments or organizations. Roles [32] are abstractions that define a set of related tasks for agents achieving their designed goals. Agents interact with other agents from the same or different environments, organizations and roles. Environments, organizations, roles and agent interactions suggest different contexts (implicit situational information [7]) found in open MASs.
2.1
Modeling Contextual Norms
Research in context-aware applications suggests top-down architectures for modeling contextual information [22,18]. Thus, DynaCROM defines that norms of MASs should be modeled according to the following four levels of abstractions: Environment, Organization, Role and Interaction contexts. We call these contexts regulatory contexts and they are differentiated by the boundaries of their data (norms). Environment Norms are applied to all agents in a regulated environment; Organization Norms are applied to all agents in a regulated organization; Role Norms are applied to all agents playing a regulated role; Interaction Norms are applied to all agents involved in a regulated interaction. Fig. 1 illustrates the boundaries of environment, organization, role and interaction norms. There, agents are regulated by compositions of contextual norms. For instance, the agents on the left side of the figure are regulated by compositions of common environment and interaction norms and by compositions of different organization and role norms; the agents on the right side of the figure are regulated by compositions of common environment, organization and interaction norms and by compositions of different role norms; the two agents interacting, from the different environments, are regulated by compositions of different environment, organization, role and interaction norms.
Fig. 1. The boundaries of Environment, Organization, Role and Interaction Norms We believe that the four DynaCROM regulatory contexts are not targeted to a particular application domain, but rather they represent a minimum set for a general contextual norm information provision in open MASs. For more complex MASs, this set should be improved with additions and refinements of regulatory contexts for representing particular domain norms. Norms define which actions are permitted, obliged and prohibited to be executed by agents in an open MAS. A permitted norm defines that an act is allowed to be performed; an obliged norm defines that an act must be performed; a prohibited norm defines that an act must not be performed. These three types of norms described represent the three fundamental deontic statuses of an act [1] from Deontic Logic [37]. Deontic Logic makes it possible to address the issue of explicitly and formally defining norms and dealing with the possibility of violation. In normative (i.e., regulated) open MASs, agents need to be norm-aware entities for taking into account the existence of social norms in their decisions (either to follow them or to violate them) and to react to norm violations by other agents [5].
2.2
Representing Contextual Norms
Norms should have their semantics explicitly expressed in a meaningful way for heterogeneous agents to process their contents. Regarding this, DynaCROM uses ontologies for representing its regulatory contexts and data. For the DynaCROM ontology, the following definitions are valid: an ontology is a conceptual model that embodies shared conceptualizations of a given domain [17]; a contextual ontology is an ontology that represents contextual information [3]; and, a contextual normative ontology is an ontology that represents contextual norm information, having the norm concept as a central asset. The DynaCROM contextual normative ontology, or simply the DynaCROM ontology, is illustrated in Fig. 2.
Fig. 2. The DynaCROM ontology The DynaCROM ontology defines six related concepts (see Fig. 2), all at the same hierarchical level, for representing its environment, organization and role regulatory contexts. The Action concept encompasses all instances of regulated actions. The Penalty concept encompasses all instances of fines to be applied when norms are not fulfilled. The Norm concept encompasses all instances of norms from all regulatory contexts. The Environment concept encompasses all instances of regulated environments; and, each environment encompasses its associated norms and its owner environment (the environment it belongs to). The Organization concept encompasses all instances of regulated organizations; and, each organization encompasses its associated norms, main organization (the organization to which it is associated) and envi-
ronment. The Role concept encompasses all instances of regulated roles; and, each role encompasses its associated norms and organization. The Norm and Penalty concepts are specialized into sub-concepts according to the permitted, obliged and prohibited statuses of an act from Deontic Logic. In order to effectively be used in an open MASs, the DynaCROM ontology should be instantiated and it probably should be extended with both particular domain concepts and interaction norms. In the DynaCROM ontology, the interaction regulatory context should be implemented by following the representation pattern [26] from a Semantic Web Best Practices document. This pattern defines that the relation object itself must be represented by a created concept that links the other concepts from the relation (i.e. reification of relationship). Thus, in the DynaCROM ontology, an interaction norm should be represented by a new Norm sub-concept linking two Role concepts. For instance, suppose that a supplier deals with a customer and the interaction between them is regulated by a norm describing the obligation to pay when a deal is done. The interaction norm in the DynaCROM ontology is represented by a new obligation concept, called for example “ObligationToPay”, linking the supplier and customer Role sub-concepts. 2.3
Composing Contextual Norms
After manually classifying and organizing user defined norms, according to a topdown modeling, and explicitly representing these norms into an ontology instance, DynaCROM uses rules to automatically compose contextual norms. This process is simple and it can be summarized as follows: DynaCROM reads an ontology instance for getting data and the information about how concepts are structured; then, it reads a rule file for getting the information about how concepts have to be composed according to activated rules; and, finally, it infers a new ontology instance based on the previous readings. Fig. 3 illustrates an overview of the DynaCROM process.
Fig. 3. The DynaCROM process DynaCROM rules are ontology-driven rules, i.e. they are created according to the ontology structure and they are limited according to the number of related concepts to which each concept is linked. DynaCROM has four pre-defined rules for hierarch its
regulatory contexts (e.g., every role has its norms composed with the norms of its organization). These rules, presented in Table 1, receive as input parameters instances of the Environment, Organization and Role concepts from a DynaCROM ontology. Rule1 (lines 1 – 4) states that a given environment will have its norms composed with the norms of its owner environment (the environment it is linked to by the “belongsTo” relationship). More precisely, the following process is executed: in (4), the owner environment (“?OEnv”) of the given environment (“?Env”) is discovered; in (3), the norms of the owner environment (“?OEnvNorms”) are discovered; finally, in (2), the norms of the owner environment are composed with the norms of the given environment. Rule2 (lines 5 – 8) states that a given organization will have its norms composed with the norms of its main organization (the organization it is linked to by the “hasMainOrganization” relationship). More precisely, the following process is executed: in (8), the main organization (“?MOrg”) of the given organization (“?Org”) is discovered; in (7), the norms of the main organization (“?MOrgNorms”) are discovered; finally, in (6), the norms of the main organization are composed with the norms of the given organization. Rule3 (lines 9 – 12) states that a given organization will have its norms composed with the norms of its environment (the environment it is linked to by the “isIn” relationship). More precisely, the following process is executed: in (12), the environment (“?OrgEnv”) of the given organization (“?Org”) is discovered; in (11), the norms of the environment (“?OrgEnvNorms”) are discovered; finally, in (10), the norms of the environment are composed with the norms of the given organization. Rule4 (lines 13 – 16) states that a given role will have its norms composed with the norms of its organization (the organization it is linked to by the “isPlayedIn” relationship). More precisely, the following process is executed: in (16), it is discovered the organization (“?Org”) of the given role (“?Role”); in (15), the norms of the organization (“?OrgNorms”) are discovered; finally, in (14), the norms of the organization are composed with the norms of the given role. Table 1. Rules for hierarch the DynaCROM regulatory contexts by composing their norms (1) (2) (3) (4)
Rule1- [ruleForEnvWithOEnvNorms: hasNorm(?Env,?OEnvNorms)
