Multifacetted modelling and discrete event simulation
Multifacetted modelling and discrete event simulation
Machine learning an artificial intelligence approach volume II
Machine learning an artificial intelligence approach volume II
Logical foundations of artificial intelligence
Logical foundations of artificial intelligence
Neural networks and natural intelligence
Neural networks and natural intelligence
Object-oriented simulation with hierarchical, modular models: intelligent agents and endomorphic systems
Representations of commonsense knowledge
Representations of commonsense knowledge
Artificial Intelligence
Reasoning about model accuracy
Artificial Intelligence
Building problem solvers
Essentials of artificial intelligence
Essentials of artificial intelligence
Qualitative reasoning: modeling and simulation with incomplete knowledge
Qualitative reasoning: modeling and simulation with incomplete knowledge
Inductive modeling of discrete-event systems: a TMS-based non-monotonic reasoning approach
Inductive modeling of discrete-event systems: a TMS-based non-monotonic reasoning approach
Model-Based Systems Engineering
Model-Based Systems Engineering
Continuous System Modeling
Theory of Modelling and Simulation
Theory of Modelling and Simulation
Simulation Model Design and Execution: Building Digital Worlds
Simulation Model Design and Execution: Building Digital Worlds
Formal Theories of the Commonsense World
Formal Theories of the Commonsense World
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The power of abstraction lies in its ability to deal with "lack" of knowledge. In this regard, success in modeling and simulation rests on discovering useful abstractions that can support objectives of modeling. In our treatment, we refer to "data abstraction" as opposed to "structure simplification" since we consider a system's behavior rather than its structure. A system's behavior can be represented as time varying input/output segments. Given the behavior of a causal, time-invariant system, we define some basic abstraction mechanisms to support inductive modeling. The basis for these abstraction mechanisms are a set of general assumptions which allow consistent abstraction of IO segments. Then, given these assumptions and non-monotonic reasoning paradigm, capable of handling them, we try to tackle the fundamental problem of insufficient knowledge in the realm of inductive modeling. In this way, by making useful abstractions, we can predict a system's unobserved behavior according to a well-defined framework of discrete-event inductive modeling.