Verifying Organizations Regulated by Institutions

Verifying Organizations Regulated by Institutions

Francesco Viganò, Marco Colombetti
DOI: 10.4018/978-1-60566-256-5.ch015
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Institutions have been proposed to explicitly represent norms in open multi-agent systems, where agents may not follow them and which therefore require mechanisms to detect violations. In doing so, they increase the efficiency of electronic transactions carried out by agents, but raise the problem of ensuring that such institutions are not characterized by contradictory norms, and provide agents with all the needed powers to fulfill their objectives. In this chapter we present a framework to verify organizations regulated by institutions, which is characterized by a precise formalization of institutional concepts, a language to describe institutions, and a tool to model check them. Finally, to evaluate and exemplify our approach, we model and verify the Chaired Meeting Institution, showing that the verification of institutional rules constitutes a necessary step to define sound institutions.
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To automate tasks whose performance is regulated by norms, like the allocation of human organs (Vázquez-Salceda, Dignum, & Dignum., 2005) or the bargain of goods in competitive markets (Noriega, 1997), electronic institutions have been proposed as a mechanism to represent norms in agent organizations (Noriega, 1997) where agents are developed by different entities and their internal states are not accessible. The term “electronic institution” is often used to refer to either the rules that regulate open multiagent systems, the organization that enforces them (Esteva, Rodríguez-Aguilar, Sierra, García, & Arcos, 2001), the software implementation of institutional rules (Esteva, Rodríguez-Aguilar, Rosell, & Arcos, 2004), or a specific formalism to describe them (Esteva, Rodríguez-Aguilar, Sierra, & Vasconcelos, 2004). For the sake of clarity, in this chapter we will adopt the term “institution” to refer to a set of rules and concepts regulating agent interactions and which may be enforced by an organization (an electronic institution according to Esteva et al. (2001)). This use of the terms “institution” and “organization” is inspired by (Searle, 1995), where an institution is any collectively accepted system of rules which creates institutional facts, and by (North, 1990), where institutions are the rules of a society regulating interactions and activities of organizations.

In contrast with (Moses & Tennenholtz, 1995), where norms (also named social laws) are assumed to be respected by agents because they are designed by a single organization, in open systems it is unrealistic to expect that autonomous agents will always comply with norms. In particular, like other social concepts (e.g., expectations (Alberti, Gavanelli, Lamma, Chesani, Mello, & Torroni, 2006) or commitments (Yolum & Singh, 2004) (Fornara, Viganò, & Colombetti, 2007)), norms describe interactions in terms of public observable entities which reflect how an agent should behave. For this reason, most efforts have been devoted to the development of normative languages amenable to automatic monitoring (e.g., (García-Camino, Noriega, & Rodríguez-Aguilar, 2005)) and tools to either avoid violations (Hübner, Simão Sichman, & Boissier, 2005) or detect and sanction them at runtime (e.g., (Esteva, Rodríguez-Aguilar, Rosell, & Arcos, 2004)). Doing so increases the efficiency of transactions carried out by agents (Noriega, 1997), but raises the problem of ensuring that such systems of rules are not characterized by contradictory norms, and allow agents to fulfill the objectives of the organization that has developed the system. This is especially important when norms of institutions are complex and cannot be reduced to a simple protocol that agents should follow, making it prohibitive to foresee all possible evolutions admitted by them.

Unfortunately, most approaches do not provide tools to automatically verify what properties are guaranteed when norms are followed or violated. As a consequence, designers can specify a set of norms which can be enforced at runtime, but they cannot check if such norms are consistent, undesirable behaviors are actually forbidden, and interactions terminate with positive outcomes when norms are correctly followed. For instance, in (Viganò & Colombetti, 2007) we showed that the rules of the Dutch Auction Institution discussed in (García-Camino, Rodríguez-Aguilar, Sierra, & Vasconcelos, 2006) admit infinite bidding rounds. The lack of tools for the verification of norms defined by institutions may be problematic, since institutions have been put forward to provide safe and reliable environments for agents (Esteva, Rodríguez-Aguilar, Sierra, García, & Arcos, 2001).

Key Terms in this Chapter

Multi-Agent System Organization: The organizational part of a multi-agent system. This part of a multi-agent system consists of entities that aim at coordinating the behavior of individual agents.

Norm-Based Artifact: An organizational artifact that is designed and implemented in terms of normative concepts.

Organizational Artifact: A (computational) entity that is designed and implemented to coordinate the behavior of individual agents. An organizational artifact is the implementation of a multi-agent system organization.

Multi-Agent Programming Language: A programming language that provides constructs to implement various multi-agent system concepts such as roles, communication, environment, resources, services, access relation, norms and sanctions.

Enforcement Mechanism: A (computational) mechanism that can enforce norms by changing the state of a multi-agent system. Such a mechanism is an integral part of multi-agent system organizations.

Regimentation: A (computational) mechanism by means of which the system is prevented to be in a violated state.

Normative Multi-Agent System: A multi-agent system in which the behavior of individual agents are coordinated by means of norms.

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