Knowledge involved in simulation modeling Application Simulation Implementation Bounded by a context
Knowledge involved in simulation modeling Application Simulation Implementation Bounded by a context …
Two phases of modeling Involving different types of knowledge and different styles of reasoning Application Conceptualization Simulation Problem Problem Definition Definition Conceptual Implementation Implementation Simulation Simulation Model(CSM) Model (ISM)
Two phases of modeling • Involving different types of knowledge and different styles of reasoning… Application Problem Definition Simulation Problem Definition Conceptual Simulation Model (CSM) Implementation Simulation Model (ISM) Conceptualization Implementation
Deeper examination of SM process SM is an interactive decision-making process Problems in Sm are usually unstructured or semi-structured, i.e. the logic relations between decision factors is not well defined or clear SM is a knowledge/ information intense process Information and knowledge are used in a contextual manner, i.e. they are related to the unique structural and behavioral characteristics of a specific application. This context is very important in deriving solutions to similar problems but very difficult to store with conventional databases /information systems a great amount of knowledge/information is embedded among the solved simulation cases. A popular approach in practice is to develop a new simulation model by retrieving "old? models developed for similar past solved problems, and modifying the"old model to solve the new case
Deeper examination of SM process • SM is an interactive decision-making process • Problems in SM are usually unstructured or semi-structured, i.e. the logic relations between decision factors is not well defined or clear • SM is a knowledge/information intense process • Information and knowledge are used in a contextual manner, i.e. they are related to the unique structural and behavioral characteristics of a specific application. This context is very important in deriving solutions to similar problems but very difficult to store with conventional databases/information systems • A great amount of knowledge/information is embedded among the solved simulation cases. A popular approach in practice is to develop a new simulation model by retrieving “old” models developed for similar past solved problems, and modifying the “old” model to solve the new case
Problems with traditional KBs: rule-based expert systems(Watson, Leake, Bachant,.) Knowledge acquisition it is difficult to obtain generalized knowledge from SM processes due to the lack of basic understanding and unstructured nature problem domain. When problem domain is not well defined, the rules formulated are imperfect and produce unreliable solutions Knowledge elicitation it is difficult and laborious to extract empirical knowledge from human experts and formalize the knowledge into decision rules that can characterize the expert performance. However many rule-based systems assumed that expert knowledge is available and can be elicited and organized efficiently
Problems with traditional KBS: rule-based expert systems (Watson, Leake, Bachant, …) • Knowledge acquisition__ it is difficult to obtain generalized knowledge from SM processes due to the lack of basic understanding and unstructured nature of problem domain. When problem domain is not well defined, the rules formulated are imperfect and produce unreliable solutions • Knowledge elicitation__ it is difficult and laborious to extract empirical knowledge from human experts and formalize the knowledge into decision rules that can characterize the expert performance. However many rule-based systems assumed that expert knowledge is available and can be elicited and organized efficiently
Problems with traditional Kbs: rule based expert systems Knowledge maintenance in many applications rules are interrelated(e.g.chained with each other) and the number of rules required are unmanageably large Results interpretation in many domains, the inference process can become complex, and it is difficult for users to understand or verify the SOlutions suggested by rule-based reasoning
Problems with traditional KBS: rulebased expert systems • Knowledge maintenance__ in many applications, rules are interrelated (e.g. “chained” with each other) and the number of rules required are unmanageably large • Results interpretation__ in many domains, the inference process can become complex, and it is difficult for users to understand or verify the solutions suggested by rule-based reasoning