1,721,094 research outputs found
The Spirit of the Place. Urban Vitality versus Industrial Decay: The Great North Experience
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Environmental indicators for the sustainable use of soil and water: some notes on a hybrid and participatory perspective
No full textSpatial cognition in indoor vs. outdoor planning: An experimental approach
No full textThe concept of spacescape has been studied by many scholars
over time (Golledge, 1998; Lloyd, 2009; Lynch, 1960). Both
landscapes and townscapes are knowledge-intensive entities that
humans adapt for their life. Because of their dynamic complexity,
spatial behaviours are hard to be simulated in mainstream AI
robotics. Therefore, a question arises about the basic features, or
‘fundamentals’, of spacescapes by agents who live in and move
through them. In fact, from the one hand, common sense claims
that well designed space architectures make space more
meaningful for humans than amorphous spaces. Conversely, the
drama of social marginality in cities also depends on the
abundance of landmarks and symbols, often inappropriate and
alienating to poor people.
Yet, a distinction emerges in literature and is worthwhile
emphasizing in general. As a matter of facts, spacescape
structural, ‘fundamental’ qualities may be opposed to spacescape
‘ornamental’ qualities in describing spaces (Goodman, 1951).
This sort of ontological representation of space is essential for
artificial intelligence and robotics, because of the inherent need
of fine-tuning the characterization of space in planning
automatic navigation. As there is circularity between AI and
cognitive science, it is evident that developing robotic devices
may in turn increase knowledge on human behaviours in space.
Therefore, space imaging can be of great interest in strategic
spatial planning, too, because it enhances the representation of
the structural, invariant, resilient characters of the environment,
for the development and management of human spaces.
The present paper looks at space ontology as made by human
agents. In doing this, it follows the cognitive approach used by
AI robotics, with integrations coming from the expert
knowledge of the planning domain. After an introduction in the
first chapter, the second chapter shows two experimentations
carried out in the context of space perception, imaging and
navigation and discusses some results achieved. Brief
conclusions and research perspectives are reported in the final
chapter
Modelos multi-agentes de la gobernanza ambiental: Notas a partir de un caso de estudio
No full textEl concepto de gobernanza comunitaria se entiende generalmente como la gestión de procesos complejos que subyacen a la complejidad del sistema ambiental. De hecho, este enfoque complejo proporciona los procesos de gestión difundidos, deslocalizados y multiagentes que tienen lugar en territorios establecidos con una visión multiscalar, multisectorial y transdisciplinaria.
Los aspectos de interés de este estudio sobre el tema de la gobernanza tratan de la definición de un sistema de agentes múltiples basado en las TIC para la representación de conocimientos, roles, relaciones, tareas y niveles operativos involucrados en los procesos de gobernanza. Este modelo se orienta hacia la construcción de arquitecturas de sistemas basadas en MAS para apoyar la formulación de políticas de desarrollo, gestionadas a través de modelos de procesos de gobernanza comunitaria
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