Programme OS4c Modelling and information
Application of multi-agent systems and ambient intelligence approaches in
Author(s): Peter Mikulecký, Daniela Ponce, Kamila Olsevicova, Jiri Haviger, Agata Bodnarova
University of Hradec Kralove
Informatics and Management
50003 Hradec Kralove
Keyword(s): water management, multi-
agent systems, ambient intelligence
Session: OS4c Modelling and information
of complex systems, interdependencies and heterogeneity of biophysical environment often lead to what are called
nonconvexities – an irregular and rugged abstract surface describing the relationship between the parameters of the
system and possible outcome states.
Interrelated socioeconomic and biophysical processes can be represented at
multiple scales which means that we incorporate complexity in natural resource use modelling.
and water is essential for capturing the dynamics of interrelated biophysical systems and it is itself a complex
Artificial life techniques are useful for incorporating complexity in ecosystem modelling in general. By
incorporating a high degree of social and spatial heterogeneity multi-agent systems could also represent “nested
hierarchies” and phenomena emerging across different scales. Artificial Life is also an appropriate approach for
capturing spatial phenomena in biophysical modelling. AL allows for the investigation of lower-level mechanisms that
might lead to the development of higher-level structural and dynamical features in landscapes.
techniques, such as Cellular Automata (CA) and Markov Models have been applied to landscape modelling as well.
The basic units for modelling locally interacting “objects” are cells on a grid, whose transition rules include their
previous state and the state of the neighbouring cells. Advanced models use Geographical Information Systems
(GIS) to store information about the state of cells in a landscape and feed this information back into the CA. The
method of CA can also be used to represent the interactions of humanlike agents in physical or social space.
Typically, the agents occupy positions on a two-dimensional grid of cells and the distances between them influence
their interactions. Some authors employ a CA framework, which can be directly linked to soil information and
Biophysical simulation models are usually calibrated at the micro-level whereas economic
models operate at a rather aggregate level. Aggregating the biophysical data so as to link it to an economic model
implies a considerable loss of statistical information. An integrated multi-agent system can, in principle, be structured
so as to perfectly match the scale and structure of available data. This is a very interesting aspect because data
disaggregation procedures are currently being developed that will help to infer micro behaviour from aggregate data
consistently. Socioeconomic and biophysical data collections at multiple spatial and temporal scales might then be
generated and fed into a multiple-agent programming model.
The paper will be oriented on summarization of
recent results in multi-agent systems application in various sub-areas of water management. As multi-agent systems
are very suitable also as a framework for ambient intelligence environment, some first ideas about exploitation of
these approaches in water management and especially for river basin management will be presented as well. All the
results are based on a series of ongoing mutually interconnected research projects.