Advanced Embodiment Design 26 March 2015 Intelligence in products, services and software Principles, techniques and applications of knowledge and information management, intelligent algorithms and artificial intelligence Advanced Embodiment Design 26 March 2015 Van der Grintenzaal Wilfred van der Vegte, Zoltan Rusak Computer-Aided Design Engineering

Overview Apparent intelligence in artefacts is typically based on embedding knowledge in it. artefact = product, software, service. particularly interesting type of software: design support  modelling, simulation, programming data  information  knowledge  wisdom increasing level of meaningfulness and utility  knowledge is supposedly the highest level that can be embedded in artefacts November 17, 2018

Types of knowledge and information According to availability to machines and humans Knowledge Conversion: process of transforming data/info/knowledge to a form in which it can be used (Nonaka & Takeuchi) explicit reproducible, recordable textual description in a book programmed instruction for a machine explicit informal knowledge formal knowledge explicit formal knowledge not processable informal formal processable a person’s skills in accomplishing a task implicit informal knowledge tacit knowledge Ø not (directly) reproducible, recordable implicit (tacit) November 17, 2018

Processing knowledge and information designers use by people use by machines design support end users products store formalize companies services make explicit explicit informal knowledge formal knowledge tacit knowledge Ø Processing knowledge and information November 17, 2018

Knowledge technologies Formalization by humans, storage and use by machines: Knowledge-based systems, Knowledge-based engineering but also: conventional embedded software in products Storage of explicit knowledge by machines: Knowledge bases, Knowledge and data warehousing Formalization by machines: Soft computing, Machine learning, Knowledge discovery and Data mining Making explicit knowledge stored in machines available to people: Search engines and Knowledge portals November 17, 2018

Formalization by humans, storage and use by machines: Knowledge-based systems/engineering Knowledge is prepared by experts (‘knowledge authors’) as rules IF material(part_A OR part_B) = thermoplastic AND no_extra_part_allowed = TRUE | THEN connection_type = snap_fit Fuzzy logic: allows rules to deal with imprecise inputs ‘large’, ‘old’, ... Rules are stored in a knowledge base (KB), an “inference engine’” extracts knowledge / finds appropriate rules in KB KBS typically focuses on specific applications, e.g.: Design support: calculating dimensions for snap-fit design; ‘Automated design’ of elevators, escalators, conveyor belts, etc. Services: Expert systems to assist in maintenance, to guide workers in call centers, etc. Products: November 17, 2018

Formalization by machines Soft computing: using inexact solutions to find exact answers (which may be correct, or correct within margins). Example: dealing with linguistic input containing imprecise terminology “serve one cold beer” (how cold is cold?) Machine learning: machines learn from data with known properties. Example: face recognition in cameras Data mining: discovering new patterns in large data sets (data with unknown properties) Example: identifying a segment of supermarket customers with similar needs and preferences SC DM ML November 17, 2018

artificial neural nets association rule learning machine learning umbrella terms methods/techniques ... fuzzy logic soft computing chaos theory bayesian networks genetic algorithms artificial neural nets association rule learning machine learning decision trees cluster analysis classification inductive logic programming sparse dictionary learning data mining regression analysis ... factor analysis outlier detection ... November 17, 2018

Automated knowledge formalization: data mining Main objective: creating knowledge from data (explicit informal knowledge) Data can be from various digital/analog sources: internet, office documents, video, images, sound (e.g., MP3 file) Mostly automatic – no knowledge authors Application areas are different: usually applied to analyse massive amounts of data, i.e. big data Typically computation-intensive  not real-time but off-line extraction for services towards companies, consumers or both November 17, 2018

Automated knowledge formalization: data mining Three steps (can appear independently): data acquisition for instance: digitizing documents or audio recordings clustering and structuring Example: Carrot2 clusters results from various search engines. representing knowledge in an explicit form to make generalization or prediction possible. November 17, 2018

Commercial applications of data mining: recommender systems November 17, 2018

Commercial applications of data mining: identifying segments of supermarket customers with similar needs and preferences This is my bonuskaart (Albert Heijn supermarket loyalty card). What is going to happen if you will all copy it from Blackboard and use it while shopping at Albert Heijn? Will Albert Heijn be pleased? November 17, 2018

Automated knowledge formalization: data mining Possible applications in design support Unlike KBE, data mining does not present a solution to a prepared design problem. any available data ↓ knowledge that might be relevant for a problem the designer could have. Advantage: outcomes can be beyond the obvious Disadvantage: outcomes might not be useful at all It might find relationships where deterministic models fail: e.g., predicting consumer taste or user behaviour Examples: image search, shape search November 17, 2018