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Background (1) Justification/Motivation/Problem (1) Project Goals (1) Methodology
Empirical Characterization (7) Conceptual Structure (13)
MASE Prototype (3) Architectural & Implementational Aspects
Case Study – the Brazilian Cerrado (2) Simulations & Results
Conclusions & Future Directions (2)
Background
• InfoKnow -‐ Computer Systems for Informa>on and Knowledge Treatment Group – Registered CNPq – Brazil’s Na>onal Council for Scien>fic & Technological Development
(hOp://dgp.cnpq.br/buscaoperacional/detalhegrupo.jsp?grupo=024010360AHR2C)
• Gerd Wagner’s visit UnB: AOR short course & talk Brazilian Symposium of Informa>on Systems (SBSI 2009)
• Agent-‐based simula>on project – Research Topic -‐ Environmental Management
• Researchers LISA -‐ Laboratory of Image, Signal and Audio – Alexandre ZagheOo & Bruno Luiggi M. Espinoza
• Students – Master – Carolina Gonçalves Abreu (graduated March 2012) – Bachelor – Cássio Giorgio Couto Coelho (graduated July 2012)
Justification/Motivation/Problem
• The paOerns of change in >me and space are produced by the interac>on of biophysical processes and socioeconomic processes
How to represent such interac8on?
• Agent-‐based modeling (ABM) & Mul>agent systems (MAS): natural metaphor for analyzing models and theories of interac>vity in human socie>es
The environment is a global challenge earth's natural processes transform local problems into interna>onal issues -‐ global
warming, hazardous waste, ozone deple>on, acid rain, air & water pollu>on, overpopula>on, rain forest destruc>on, Brazilian Cerrado/Woodland Savanna destruc>on
Land Use/Cover Change (LUCC): major sources of change in terrestrial surface
Few empirical models and tools to
simulate, analyse & forecast environment impacts
Project Goals
Considering the motivation/problem: Study & define empirical characterization model
understand how is the behaviour of human agents, the physical environment and policy decisions how these affect the dynamics of Land Use/Cover Change (LUCC)
Develop a multiagent simulation tool to explore different scenarios, useful for planning, development sustainable LUCC strategies
Develop a case study
Methodology
1) Empirical Characterization Comprehensive framework of Smajgl et al. (2011) - set most commonly used
empirical methods for the challenge of parametreize behavioural responses of humans: 1. expert knowledge, 2. participant observation, 3. social surveys, 4. interviews, 5. census data, 6. field or lab experiments, 7. role-playing games, 8. cluster analysis, 9. dasymetric mapping, 10. Monte Carlo method.
(Smajgl, A.; Brown, D. G.; Valbuena, D.; Huigen, M. G. A., 2011. Empirical characterisation of agent behaviours in socio-ecological systems. Environ. Model. Softw. 26, 837-844)
Empirical Characterization
Smajgl et al. (2011) cites ABM requires systematic representation of 3 main phenomena:
• agents, • their social networks & • agent environment. Agent parameterization uses 6 methodological steps (each with multiple empirical
methods)
Empirical Characterization
MASE parameterization & the empirical methods (Smajgl et al., 2011):
• M1 – identify different classes of agents – Agents share same sequence of actions, or represent same modeled behaviour, defined in an agent class – Each agent class grouped into agent types (i.e., sub-classes), classification Russell & Norvig (2009):
simple reflex, goal-based, utility-based agents – 2 sub-classes reflex agents: service (e.g., configure simulation grid) & transformer (farmers, ranchers,
urbanization, conservative agents) – 1 sub-class goal based agent: managers (e.g.,grid, spatial-cell agent & transformation)
• M2 – specify values for agents attributes based on real data – Agent types behaviour – qualitative contextual variables (expert knowledge) & quantitative census data
(e.g., sub-class transformer agents different behavioural responses tax of land use for farmer & urban)
Empirical Characterization
MASE parameterization & the empirical methods (Smajgl et al., 2011): • M3 – specify parameters for the agents behavioural rules based Smith et al. (1998),
FSM – agent’s exploration behaviour
• M4a – agent types defined from agent attributes combining mix of correlation and expert knowledge (Federal District Spatial Plan-PDOT sets areas to agriculture...)
• M4b – alternative method to define agent types is participant observation
(Smith, J.; Winograd, M.; Gallopin, G.; Pachico, D., 1998. Dynamics of the agricultural frontier in the amazon and savannas of brazil: analyzing the impact of policy and technology. Environmental Modeling & Assessment 3, 31-46)
Empirical Characterization
MASE parameterization & the empirical methods (Smajgl et al., 2011): • M5 – assign agents in the whole population to the appropriate agent type
– Agriculture census of 2006 by Brazilian Institute of Geography & Statistics (IBGE)
Empirical Characterization
Our expericence with the Smajgl et al. (2011) parameterization for ABM:
• Initial structure can be used within rigorous discussions of parameterization methods for human behaviour
• Use of M1-M5 method allow a robust empirical model development – The sequence facilitates the description & documentation of ABM
applications • We expect the model description would allow peers judgments about the value
of models/results (e.g., the Mase)
Methodology
2) Conceptual Structure – Normal critics on documentation of I/ABM, lacks in consistency in text description of
such models, i.e., they don’t provide enough information to replicate results, difficult to read, not presented logical order (Lorek & Sonnenschein, 1999)
– To address this problem: Overview, Design Concepts & Details (ODD) protocol for documenting I/ABM (Grimm et al., 2006)
(Lorek, H.; Sonnenschein, M.,1999. Modelling and simulation software to support individual-based ecological modelling. Ecological Modelling 115 (2-3), 199-216)
(Grimm, V.; Berger, U.; Bastiansen, F.; Eliassen, S.; Ginot, V.; Giske, J.; Goss-Custard, J.; Grand, T.; Heinz, S. K.; Huse, G., 2006. A standard protocol for describing individual-based and agent-based models. Ecological Modelling 198 (1-2), 115-126)
Conceptual Structure Overview - State variables & scales The interactions between landscape & humans come from cycles of agents’perception & action based
on Haggith et al. 2003 & Le et al. 2008
(Haggith, M.; Muetzelfeldt, R. I.; Taylos, J., 2003. Modelling decision-making in rural communities ant the forest margin. Small-scale Forest Economics, Management and Policy 2 (2), 241-258)
(Le, Q. B.; Park, S. J.; Vlek, P. L. G., Cremers, A. B., 2008. Land-Use Dynamic Simulator (LUDAS): A multi-agent system model for simulationg spatio-temporal dynamics of coupled human-landscape system. I. Structure and theoretical specification. Ecological Informatics 3 (2), 135-153)
Conceptual Structure
Overview - State variables & scales • The temporal and spatial scale of MASE model is configurable. • In the case study, the model operates on local scale of regions with areas of the
Federal District of Brazil (adapted the land use model defined by Smith et al. 1998)
(Smith, J.; Winograd, M.; Gallopin, G.; Pachico, D., 1998. Dynamics of the agricultural frontier in the amazon and savannas of brazil: analyzing the impact of policy and technology. Environmental Modeling & Assessment 3, 31-46
2002 2008
Conceptual Structure Overview - State variables & scales
• A cell in the grid represents 1 hectare • Each time-step is one week of chronological time • Simulations were carried out involving period 2002-2008 (365 time-steps or 7 years) • Used satellite images of LANDSAT ETM (NASA/INPE) classified by PROBIO software (IBAMA - The Brazilian Institute of Environment and Renewable Natural Resources)
• Anthropic (yellow), remaining (green), water (blue)
Conceptual Structure
Overview - Process overview & scheduling
Process overview – a verbal & conceptual description of each process & its effects • In the implementation of MASE model:
– the agent interactions succeed every time-step – Initial grid of simulation (landscape) used images GIS raster files – the initial population of each type of agent is configurable (by users) – In each step it is updated the state of the environment followed by the decision-
making of transformation agent
Scheduling – the order of the processes, the order in which the state variables are updated • State variables and scales in MASE
– time is modeled by discrete intervals (time-steps) – all activities take place in an atomic fashion, i.e., only after all actions have been
deliberated & execute by agents occurs the transition to a time-step – Variables are updated synchronously in a time-step
Conceptual Structure
Design Concepts • Provide a common framework for designing and communicating I/ABM • The purpuse link model design to general concepts of Complex Adaptive
Systems (Grimm & Railsbach 2005, Railsback, 2001) • Provide a short checklist of 5 items that apply to MASE model:
– emergence – human agents population dynamics emerge from the behaviour of each agent at the land use decisions (agent class)
– sensing – each transformation agent has corresponding capacity conversion/exploitation of the environment
– fitness – the achievement of objectives is explicity modeled in the transformation agents decision process of land use
– Interaction – • Indirect – changes in land use caused by an agent may lead to changes in the decisions of
other agents in the next time-step • Direct – the race for space exploration by agents (e.g., same exploration probability of
one cell space, rules of succession between different agent classes – farmers, ranchers or chosen randomly among agents of the same class)
– Observation – maps of successive coverage & land use for each time-step
Conceptual Structure
Technical Details • The ODD sequence include 3 elements:
– Initialization • Inital landscape model – images raster-GIS obtained by satellite monitoring
(LANDSAT ETM), state variables & scales (overview) • Physical environment is imported into a set of images (proximal variables) • Time-steps: t=0 (2002 map) & t=6 (2008 map) • Transformation agents set by user (# of farmers, ranchers, IBGE rural census) • Agents alocation - greater likelihood of exploitation
– Input • Images are classified for time-step t=0, set of images for each variable
proximal set, set of policy information to be exploited (PDOT) – Red – urban macrozone – Blue – controlled use – Green – diversified use
Conceptual Structure
Technical Details Input • Proximal variables: (a) railways, (b) highways, (c) rivers, (d) lakes, (e) streets, (f) buildings & protected
areas.
Conceptual Structure
Technical Details • Input
Physical environment – use 6 image layers (proximal variables) Images were filtered using Gaussian bi dimensional filter (Equation 1)
Proximal variables-‐ streets
+p positive influence -p negative influence 1 Neutral
magnified por>on of a proximal variable
Conceptual Structure Technical Details
After filtered images are generated, compute weighted average: Where: T = resulting map, Mi = filtered image related to proximal variable i and pi (i.e.,indicates how variable i contribute in the generation of the map T), all pi weights equal to 1/6 Use Taux map showing the disabled regions for any use (e.g., protected area, urbanized)
Conceptual Structure
Technical Details How T & Taux maps are used to determine movement of transformation agent? • (a) T map: green potentially be explored & yellow explored areas • (b) Taux map: logical value 1 candidates to move & 0 no exploration
Conceptual Structure
Technical Details The new destination for the transformer agent is determined - probabilistic manner • (c) superimposition of T and Taux map • (d) normalize the vector values V[i]=[96, 21, 98,...] Then calculate C[i] function:
Finally, pseudorandom number N is drawn from the standard uniform distribution open interval (0,1) and searches for the first value of i where C[i] > N
Conceptual Structure
Technical Details Submodels • FSM used in the management of transition states or grid cells (Smith et al. 1998)
Smith, J.; Winograd, M.; Gallopin, G.; Pachico, D., 1998. Dynamics of the agricultural frontier in the amazon and savannas of brazil: analyzing the impact of policy and technology. Environmental Modeling & Assessment 3, 31-46)
MASE Prototype Multiagent Project • Tropos methodology, focus requiriments (Giorgini et al., 2004) • Environment characterization (Wooldridge, 2009)
– Real model: partially observable, stochastic, sequential, dynamic, continuous – Implemented prototype: partially observable, deterministic, episodic (steps), static, discreet
(FSM), multiagent and competitive • Agent characterization
(Giorgini, P.; Kolp, M.; Mylopoulos, J.; Pistore, M., 2004. The Tropos Methodology: an overview. Kluwer Academic Publishers) (Wooldridge, M, 2009. An Introduc>on to Mul>Agent Systems. John Wiley & Sons, LTD, Chichester, England, 2nd edi>on)
Architecture
Implementation Aspects
Used Java Agent Development Framework – JADE, version 4.0, release date 04/20/10 (Bellifemine et al., 2007)
Foundation for Intelligent Physical Agents – FIPA compliant Agent Communication Language (ACL) defined by FIPA Agent Management System (AMS) – white pages Directory Facilitator (DF) – yellow pages Agent Behaviour
simple approach - OneShotBehaviour, CyclicBehaviour Composed approach – SequentialBehaviour and FSM-Behaviour
Library ImageJ to process image files (MatLab) Machine – Intel Core i5 2.27 GHz 4 GB RAM, 64-bit operating system
(Bellifemine, F. L., Caire, G., Greenwood, D., 2007. Developing Mul>-‐Agent Systems with JADE. John Wiley & Sons)
Case Study – Brazilian Cerrado
Cerrado is major Brazilian savanna-like ecosystem • 2nd largest Brazilian biome, 204.7 million hectares hotspot for conserva>on 68.11% of na>ve vegeta>on cleared Federal District was used: the single unit in Brazil fully covered by the Cerrado Figure presents overview of the Cerrado are and its land uses (Sano et al., 2008)
(Sano, E. E., Rosa, R., Brito, J. L. S., Ferreira, L. G., 2008. Mapeamento semidetalhado do uso da terra do bioma cerrado. Pesquisa Agropecu-‐ ária Brasileira 43 (1), 153–156)
Simulations & Results
Developed many simulations, two most important: • Fixed exploration rate, number of transformation agents varying from 10 to
150 farmers and ranchers (40 min for 365 steps, 2002 to 2008) – Best result - 99,87% with 90 farmers and 90 ranchers
• Variable exploration rates and percentage of group agents varying from 5% to 50% (60 min for 365 steps) – Best result – 99,13% with 10% transformation agents exploring 1500 units and 90% exploring
500 units
Conclusions
• Ongoing project • Defined MASE model in a systematic and structured way:
– Empirical characterization - Smajgl et al. (2011) framework model for ABM – Conceptual structure - Grimm et al. (2006) ODD protocol
• Implemented MASE prototype - multiagent simulation tool – to explore different scenarios – useful for planning, development sustainable LUCC strategies
• Developed a case study to validate MASE • MASE presented good simulation results
– image correctness > 99% (2002-2008 Federal District LUCC) – Veldkamp & Lambin (2001) simulation LUCC models satisfactory success rate with 85% correction
(Veldkamp, A., Lambin, E. F., 2001. Predic>ng land-‐use change. Agriculture, Ecosystems and Environment 85 (1–3), 1–6.)
Future Directions
The defined empirical characterization for MASE • Initial structure to be used within different parameterization methods for
human behaviour • Need to check other methods Search how to improve the rationality of agents • Develop a BDI model? • Use foundational ontology? • Use qualitative spatial/spatio-temporal reasoning, spatial ontologies?
(Leeds University-Tony Cohn’s Region Connection Calculus-RCC) Do prospective research about AOR modeling and simulation • Model MASE Case Study in AOR to understand AORSL • Study how to extend AORSL, possibly to integrate to MASE
Célia Ghedini Ralha [email protected]
