Organisation Models

An organisation is defined as “an organised group of people with a particular purpose, such as a business or government department” (Oxford University Press2018). This definition can easily be mapped to multi-agent organisations representing an organised group of agents with a particular purpose. Most relevant in this context is the purpose of the organisation which motivates the existence of the organisation in the first place.

Organisation models can be found in the areas of organisation management, multi-agent re-search and robotics rere-search. The main intention of existing rere-search approaches lies in the formalisation of goal driven organisations, which are formed by a number of virtual or physical agents, e.g., software agents, robots, or humans. Existing organisation models do not account for changing agents at a microscopic level, i.e., they do not change or adapt agent’s internals.

However, agent behaviour can be adapted or rather enforced through the implementation of organisational structures and norms, such as interaction rules.

OMNI The organisation modelling approach Organisational Model for Normative Institu-tions (OMNI)(V. Dignum2009) is a formal approach from organisational research, and it al-lows to check the conformance of the behaviour of agents with a set of organisational rules.

The research aroundOMNIgenerally focuses on “a human-centred perspective, where norms may be violated” (Putten et al.2009). Correspondingly, an agent rather represents a real per-son in this context. The formalisation of a model allows to monitor the actual work practice in a (human) organisation and compare it with the intended agent behaviour, so that behaviours outside the norm can be penalised or sanctioned. The need for such a strong external organisa-tion control, arises from a strong autonomy assumporganisa-tion of agents, i.e., each agent can operate autonomously and with its own agenda, yet, has to follow the organisation’s rules.

Therefore OMNI uses information about non-conforming behaviour to sanction agents and thereby control or rather enforce the agents’ contribution to an organisation’s goal. OMNI is the combination of two separately developed models: OperA (V. Dignum, F. Dignum, and Meyer2004; Putten et al.2009) and HarmonIA (Vázquez-Salceda and F. Dignum2003). OperA represents a top-down modelling approach to describe structure and goals of an open agent society, while HarmonIA is a formal framework to implement norms in organisations which are participating in online marketplaces and using electronic transactions (F. Dignum2001).

Through the combination of these two existing models,OMNI inherits an organisational, an ontological and a normative dimension (also referred to as deontic dimension by Hübner, Sich-man, and Boissier (2004)). OperA provides the organisational dimension which can be further split into three models: (1) an organisation model which captures the organisational structure using roles and interaction templates called scene scripts, (2) a social model which defines the responsibilities that come with a role and the required capabilities for a role, and (3) an

interaction model to represent bilateral agreements between agents to define their pairwise interaction. The normative dimension is originally found in HarmonIA, which defines how abstract norms can be activated in an agent society. HarmonIA’s approach follows an iterative approach to concretise abstract norms, first into rules and policies, and finally into concrete procedure implementations.

The usage of ontologies is an integral part of OperA and HarmonIA and V. Dignum (2009) see the integral use of ontologies as advantage over other approaches, where ontologies are only seen as external components. Hence, OMNI comprises an additional ontological dimension to establish the common understanding in an open agent architecture, i.e., the ontological dimension defines how and what can be communicated between agents so that a knowledge exchange can be achieved.

MOISE+ Hübner, Sichman, and Boissier (2004) developedModel of Organisation for multI-agent SystEms (MOISE+)as an organisation model with a focus on the reorganisation capability of multi-agent systems. Their design philosophy is based on three dimensions of an organisa-tion: (1) structural, (2) function, and (3) deontic (normative). Hübner, Sichman, and Boissier assume an organisation which imposes constraints on its member agents, and each organisa-tional dimensions brings its own set of constraints or restrictions: the structural dimension, e.g., defines how the organisation is divided into groups, or what kind of roles agent fulfil. The functional dimension involves behavioural templates or rather plans, which can be followed by an agent to perform a task. The deontic dimension defines a set of social interaction rules, which the agents have to follow during operation. The organisation thrives towards a goal, and the combination of restrictions in the three dimensions controls the observable organisational behaviour.

With this setupOMNIandMOISE+have a very similar decomposition, but Hübner, Sichman, and Boissier focus on reconfiguration of the organisation: an optimal team structure depends on the environmental context and the goal. Hence, reconfiguration can allows to adapt and thus optimise the agent team structure.

A transition from one team structure to another can be planned or unplanned: planned transi-tions can be triggered in a top-down fashion by an external operator, or they can be scheduled for a specific time. Hübner, Sichman, and Boissier require planned transitions to follow a pre-viously defined and therefore static reorganisation pattern, while unplanned transitions have to be dynamically controlled by agents.

Generally however, a transition follows a phase pattern for organisation change, original de-fined by So and Durfee (1993) for distributed networks. The pattern consists of: (1) amonitor phase, (2) adesign phase, (3) an evaluate and select phase, (4) and an implement and execute . This pattern is similar to the four stage model of teamwork by B. M. Dunin-Keplicz and Rineke Verbrugge (2010) with the corresponding stages: potential recognition, team formation, plan formation, and team action. In other cases reconfiguration can be also viewed as preparation of the team for a new task. The reorganisation process in MOISE+ itself requires forming a predefined group structure: one agent has to adopt the role of the so-called OrgManager in order to organise the overall reconfiguration. The reconfiguration group also requires at least one agent to take over theDesignerrole, in order to analyse the current status of the organi-sational structure, and suggest a potentially better structure. By encoding the required tasks for reconfiguration as agent roles, Hübner, Sichman, and Boissier identify the core elements

of a general reconfiguration recipe for distributed teams. They illustrate their approach using a robot soccer simulation, i.e., involving 11 agents, and apply Q-learning to identify the best reconfiguration policy for a game, where the opponent maintains a static team organisation.

They show, however, no experiments with real robotic systems.

OMACS In the area of robotics Organisation Model for Adaptive Computational Systems (OMACS) is another approach for designing an organisation model presented by DeLoach (2009) and Deloach, Oyenan, and Matson (2008). The main concepts in OMACS are goals, roles, agents and capabilities. OMACSuses a capability-based representation for a role, i.e., a role is defined by a set of capabilities, and the quality of an agent’s capability can be quantified using values normalised to [0,1]. In the same way DeLoach quantify an agent’s ability to fulfil a role based on its capabilities. Independant of the agent definition, OMACS accounts for a degree of suitability of a role to achieve a goal, and by combining the information about roles, agents and goalsOMACS allows to quantify the quality of an overall agent assignment with respect to goals.

The model assumes atomic capabilities without composition, and the value normalisation to [0,1] restricts the quantification, e.g., for a qualification of capability, to a single dimension.

The quality of an agent’s capability has therefore (initially) unclear semantics, which limits the applicability of the approach in practical applications.

Similar to the deontic dimension in MOISE+, DeLoach suggest the use of behaviour policies to control the cooperative behaviour of agents. InOMACSan organisation designer can explicitly define reorganisation rules. For instance to specify if and how one agent can replace another agent once the latter becomes unable to fulfil a role. An application of runtime reorganisation has been shown with three real robots, and a single laptop agent by Zhong and DeLoach (2011).

The robots perform dynamic reorganisation to maintain a general patrolling task either after an agent fails to communicate or after the degradation of a capability which is required for the patrolling task. The scenario has been verified in simulation using eleven robots.

Summary OMNI and its comprising modelling approaches are frameworks for the specifica-tion and design of open multi-agent organisaspecifica-tions. They implement a rigid formal frame for autonomously acting agents, which are mostly human, in order to achieve organisation goals.

This is a valid approach for organisations with low control on the internal design of agents, e.g., as it is true for human agents.

The main missing element however in OMNI is an explicit accounting for the dynamics of change in an organisation. This is done byMOISE+. It implements a pattern to control the re-configuration process. Therefore it can continuously optimise the organisation to achieve the organisation’s objectives. Similarly, and applied to robotics,OMACSsets the main focus on the quantification of the potential of abstract roles and agents to contribute towards an organisa-tion’s success. The usage of this information allows to improve the team structure to increase the likelihood of an organisation’s success. Generally, however, organisation modelling ap-proaches have been limited to reconfiguration as reassignment of tasks to systems. They do not account for any type of superadditive effects.