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Presentation of my projects in modeling domain
Index
Presentation of my projects in modeling domain
- Aggregates exploitation and transport flow (France)
- Artisanal Gold Mining (Burkina Faso)
**    Paper Award
- Ultra-pure Quartz exploitation (Madagascar)
- Collective management of animal wastes (La Réunion)

Research interests

My general research interests are about the modeling of socioeconomic complex systems by using the multi-agent system (MAS) based approach. The application domain is natural resources management in Africa and in France.

Apart from the multi-agent domain itself, I am also curious about:
- general aspects of psychology such as conscious/unconscious mind and human/animal behaviors, etc.
- the possible connection of Multi-Agent System with other concepts such as Geographical Information System (GIS), Mathematical Models, etc.

What is Multi-Agent System (MAS)?

multi-agent systemIn the field of Distributed Artificial Intelligence, MAS is often used to model complex systems for many reasons. In particular, the MAS approach is adequate for situations where it is impossible to obtain all output data from a purely mathematical or statistical transformation of input data. One may cite the example of human society, where the overall behavior of a society is not the sum of the individual behavior of the agents of which it is composed. MAS is an alternative to the so-called “classical approach” such as the differential equation.

An agent can be a physical or virtual entity that can act, perceive its environment (in a partial way) and communicate with others, is autonomous and has skills to achieve its goals and tendencies. It is in a multi-agent system (MAS) that contains an environment, objects and agents (the agents being the only ones to act), relations between all the entities, a set of operations that can be performed by the entities and the changes of the universe in time and due to these actions.

The notion of autonomy in agents gives the user the possibility of simulating the behavior of humans and/or animals, which is conceptually difficult, by trying to introduce and to follow all possible behavior of individual agents in a centralized manner, especially when the number of agents increases.

The view of economy as a complex system and the permanent existence of society in which economic actors evolve probably constitute one of the motivations for MAS being used for modeling socio-economic systems.

Insofar, I have used three MAS platforms: ADK, GEAMAS and CORMAS, as explained below and have been involved in the following projects in modeling:
- Aggregates exploitation and transport flow (France)
- Artisanal Gold Mining (Burkina Faso)
- Ultra-pure Quartz exploitation (Madagascar)
- Collective management of animal wastes (La Réunion)

The ADK/RDK platform

ADK (for "Agent Developer Kit") is developed with the idea of simulating a society of artificial agents. ADK contains three of the main components generally found in Multi-Agent Systems: agents, objects and environment containing entities that can be agents or objects. Our present work is based on a specialization of ADK to the world of robots: RDK (for “Robot Developer Kit”).

The architecture of an individual ADK agent contains two main parts:
- the body, which is in relation with the environment and performs both perception and action phases.
- the head, which contains the deliberation phase.

ADK architectureLike individual agents, group agents also have two parts:
- the group body, which physically contains the properties of the group. It can be any object in the environment, chosen by the user of the application.
- the group head, which is the “set” of the part of the head of individual agents composing the group, but which plays roles related to the interest of the group.

ADK is reorganized as four layers that are, from top to bottom:

- the behavior layer that groups all individual and group head activities. It is based upon the fact that every agent can generically play any role and can possess any objects.

- the agent layer that is the previous agent body. Its relation with the above layer is represented by a link named adopts, which is actually the abstract formulation of the deliberation process described previously.

- the object layer, containing all objects of the system. The agent layer is connected to this layer via two links. The first link is named has. A link agent.has(object) exists if agent actually possesses object. The second one is manipulates which represents the manipulation of object by agent (e.g. the tasks in the ASGMA case). The instantiation of this link is initially preset by the user.

- the space layer, which is a 2D continued environment represented by (x, y) coordinate via a link named is_at. Agents and objects situated in the space layer take geometrical forms such as a circle, polygon, etc.

The GEAMAS platform

GEAMAS (for "GEneral Agent for Multi-Agent System") is a knowledge engineering environment for multi-agent simulation of complex systems. The basic architecture of GEAMAS is designed around three dimensions: MultiAgent Systems (MAS) software design, MAS knowledge abstraction and MAS services dimension. Each one of this three aspects, is implemented into several modular open software layers.

The GEAMAS agent model is based on three agent levels. Each knowledge layer successively implements the different abstraction levels of describing the agent structure:

geamas- the macro level, sometimes called Society, describes the modeled system in its coarsest granularity. The MAS contain only one macro-agent which represents the whole of the system. It possesses a global knowledge of the system and the sub-systems of which it is made up.

- the micro level corresponds descriptively to the finest granularity. Each micro-agent is an autonomous entity describable by its interactions with the other agents, its behavioral characteristics and its capacities for evolution. These agents communicate by messages asynchronously transmitted throughout their acquaintanceship network. Depending upon its state, an agent can decide to react or not in order to optimize an internal satisfaction function. At this level it is important to apply the principle of omission (detail masking) in order to master the complexity of the model to avoid a multiplication of micro-agents.

- the medium level is descriptive of any eventual intermediary structures, which are considered as sub-systems of the global system. The medium agents exist only in relation to the Micro-level of which they are abstractions. As autonomous structures, they can impose constraints upon the micro-agents through the Composition and Decomposition mechanisms.

The CORMAS platform

CORMAS (for "Common-Pool Resources and Multi-Agent Systems" is a programming environment dedicated to the creation of multi-agent systems, with a specificity in the domain of renewable natural-resources management. Designed by CIRAD (for International Center for Agronomical Research Development), Cormas provides a framework for developing simulation models of coordination modes between individuals and groups that jointly exploit common resources. The framework is structured in modules.
- the first module allows the definition of the entities of the system to be modeled, which are called informatics agents, and their interactions. These interactions are expressed by direct communication procedures (sending of messages) and/or by the fact of sharing the same spatial support.
- the second module deals with the control of the overall dynamics (ordering of different events during a time-step of the model).
- the third module allows the definition of an observation of the simulation depending on viewpoints. This feature allows the integration, within the modeling process, of representation modes.
Cormas facilitates the work of constructing a model by offering predefined elements within these three modules.

Among these items are the Cormas entities, which are generic classes from which, by specialization and refining, the user can create entities specific to the needs of its application.
Cormas proposes three classes of entities:
- spatial entities (elementary or composite): the design of the spatial support rests on spatial entities, which are themselves agents;
- social entities, which can be simple, situated and/or communicative,
- passive entities (messages and objects)

The applications

Insofar, I have been involved in the following application projects in modeling:
- Aggregates exploitation and transport flow (France)
- Artisanal Gold Mining (Burkina Faso)
- Ultra-pure Quartz exploitation (Madagascar)
- Collective management of animal wastes (La Réunion)