AI-ordlista
Den kompletta ordlistan över AI
Multi-Agent System (MAS)
Set of autonomous agents interacting with each other within a shared environment to collectively accomplish complex tasks. MAS allow modeling emergent phenomena impossible to predict from the individual behavior of agents alone.
Microsimulation
Simulation technique modeling each individual entity of a system with its own characteristics and behaviors. Microsimulation allows studying the impact of policies or changes at the individual level on global dynamics.
Agent-Based Modeling (ABM)
Computational modeling approach simulating the actions and interactions of autonomous agents to evaluate their effects on the global system. ABM allows exploring how individual behaviors generate complex patterns at the collective level.
Virtual Environment
Computational space in which agents evolve, defining spatial, temporal constraints and available resources. The environment influences and is modified by the actions of agents, creating a co-evolution dynamic.
Interaction Rules
Set of protocols defining how agents communicate, collaborate or compete with each other within the system. These rules determine the mechanisms of information transmission and influence between autonomous entities.
Agent State
Set of attributes, internal variables and parameters defining the current condition of an agent at a given moment. The state evolves according to internal transition rules and interactions with the environment and other agents.
Interaction Topology
Spatial structure or network defining which agents can interact directly with each other at a given moment. The topology strongly influences the propagation of information and the formation of behavioral clusters in the system.
Model Calibration
Process of adjusting model parameters to minimize the gap between simulated results and observed reference data. Calibration ensures the predictive relevance of the model in relation to real phenomena.
Agent Heterogeneity
Diversity of characteristics, behaviors, and decision rules among different agents in the system. Heterogeneity is essential for capturing real complexity and generating non-trivial emergences in simulations.
Cellular Automaton
Discrete model where cells on a grid evolve according to states determined by their immediate neighbors' states. Cellular automata constitute a simplified form of agent-based modeling for studying the emergence of complex patterns.
Complex Adaptive System (CAS)
System composed of many interacting agents that adapt and learn from their experiences, modifying their behaviors and internal rules. CAS exhibit properties of self-organization and continuous emergence.
Bottom-Up Modeling
Constructivist approach starting from microscopic individual behaviors to generate and understand macroscopic phenomena. Bottom-up modeling contrasts with the traditional top-down approach in social sciences.
Global Properties
Emergent system characteristics observable only at the collective scale, not reducible to the simple sum of individual properties. These properties result from complex interactions between agents and define the global behavior.