1. Fundamental representation issues
  Management of spatio/temporal knowledge (spatio/temporal reasoning, extension of GIS capabilities, reasoning across several levels of temporal or spatial granularity).
  Management of uncertainty and imprecision (fuzzy logic, belief functions, Bayesian networks, qualitative reasoning).
  Modeling of Multi-agent systems (population modeling, distributed problem solving).
2. Tools, techniques, algorithms
  Use of emerging technologies (neural network, genetic algorithms, fuzzy control, constraint satisfaction, case-based reasoning).
  Fusion of multiple sources of information (in particular for interpretation systems).
  Rise of AI in simulation tools (discrete event systems, use of advanced representation capabilities, qualitative/quantitative simulation).
  Integration/combination of AI approaches with other approaches.
3. Applications and practical issues
  Successful or novel AI decision support approaches for planning, scheduling, control, monitoring, prediction or diagnosis problems in agriculture and related domains.
  Commercialized and routinely used AI-based systems, in particular expert and knowledge-based systems (in laboratories, extension services, farms).
  Critical analysis of project failures.
  Validation issues for AI-based decision support systems.
  Intelligent information systems, and web intelligence.

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