Expert system case study ppt

expert system in agriculture ppt and expert system powerpoint presentation
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Dr.BenjaminClark,United States,Teacher
Published Date:21-07-2017
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Intelligent Control and Cognitive Systems brings you... Cognitive Architectures Joanna J. Bryson University of Bath, United KingdomFrom Last Week Combinatorics is the problem, search is the only • solution. The task of intelligence is to focus search. • Called bias (learning) or constraint (planning). • Most `intelligent’ behavior has no or little real- • time search (non-cognitive) (c.f. Brooks IJCAI91). For artificial intelligence, most focus from design. •Architectures What kinds of parts does the system need? • Ontology • How should those parts be put together? • Development methodology • How exactly is the whole thing arranged? • Architecture •“Architectures?” Like reactive planning, the term cognitive • architecture doesn’t quite mean what its component words do. People have been looking for a generic plan • for building “real” (human-like) AI. This used to be a popular area of research, • now gets fewer publications. Nevertheless, evolutionary history tells us • something about what worked & didn’t.What Worked The past does not • necessarily predict the future, particularly in AI. Changes in hardware • and other tech change what is possible.Cognitive Architecture Where do you put the cognition? • Really: How do you bias / constrain / • focus cognition (learning, search) so it works?Basic Unit– Production From sensing to action (c.f. Skinner; • conditioning; Witkowski 2007.) These work basic component of • intelligence. The problem is choice (search). • Require an arbitration mechanism. •Production-Based Architectures arbitration mechanisms Expert Systems: allow choice of • policies, e.g. recency, utility, random. SOAR: problem spaces (from GPS), • impasses, chunk learning. ACT-R: (Bayesian) utility, problem • spaces (reluctantly, from SOAR/GPS.)Expert Systems Idea: Encode the knowledge of a • domain expert as productions, replace them with AI. Big hype in 1980s, do still exist e.g. for • checking circuit boards, credit / fraud detection, device driver code. Problem: Experts don’t know why they • do what they do, tend to report novice knowledge (last explicit rules learned.)General Problem Solver GPS, written by Newell, Shaw & Simon • (1959, CMU), first program that separated specific problem (coded as productions) from reasoning system. Cool early AI, but suffered from both • combinatorial explosion and the Markov assumption. Soar was Newell’s next try. •Productions Soar • operate on a predicate database. If conflict, • declare impasse, then reason (search harder). Remember • resolution: chunkSoar has serious • Soar engineering. Contributing Soar Major Example Implementation Ideas Version Results Systems “Evolution of • Goal Substate MOUTBOT Decision Cycle Soar8 - 1999 SGIO Dependency Coherence QuakeBot Soar” is a Improved TacAir-Soar TCL/Tk Soar7 - 1996 Interfaces RWA-Soar Wrapper favourite AI High Air-Soar Soar6 - 1992 C Efficiency Instructo-Soar paper (Laird & External Air-Soar Destructive Single State Soar5 - 1989 Tasks Hero-Soar Operators Rosenbloom ET-Soar External UTC Soar4 - 1986 NL-Soar Release 1996) – admits General R1-Soar Chunking Soar3 - 1984 Learning problems & Universal R1-Soar OPS5 Preferences Subgoals Soar2 - 1983 Subgoaling Dypar-Soar Lisp mistakes Production Universal XAPS 2 Weak Soar1 - 1982 Toy Tasks Systems Weak Method Lisp Methods Symbol Heuristic Problem Systems Search Spaces Not enough 50 • applications for ← One problem: main ap / funding human-like AI is war games for US military.Architecture Lessons (from CMU➣Michigan) An architecture needs: • action from perception, and • further structure to combat • combinatorics. Dealing with time is hard (Soar 5). •ACT-R Learns (& executes) • productions. For arbitration, relies • on (Bayesian probabilistic) utility. Call utility “implicit • knowledge”.ACT-R Research Replicate lots of Programme • IntentionalModule Cognitive DeclarativeModule (notidentified) (Temporal/Hippocampus) Science results. GoalBuffer RetrievalBuffer (DLPFC) (VLPFC) See if the brain • Matching(Striatum) does what you Selection(Pallidum) think it needs to. Execution(Thalamus) Win Rumelhart • VisualBuffer ManualMotor (Parietal) (Motor) Prize (John Anderson, VisualModule ManualModule (Occipital/Parietal) (Motor/Cerebellum) 2000). ExternalWorld Productions (BasalGanglia)Architecture Lessons (from CMU Ψ) Architectures need productions and • problem spaces. Real-time is hard. • Grounding in biology is good PR, may • be good science too. Being easy to use can be a win. •Spreading Activation Networks “Maes • Nets” (Adaptive Neural Arch.; Maes 1989, VUB) Activation spreads • from senses and from goals through net of actions. Highest activated • action acts.Spreading Activation Networks Sound good: • easy • brain-like (priming, action potential). • Still influential (Franklin & Baars 2010, • Shanahan 2010). Can’t do full action selection: • Don’t scale; don’t converge on • comsumatory acts (Tyrrell 1993).Tyrrell’s Extended Rosenblatt & Payton Networks Consider all information & all possible • actions at all times. Favour consumatory actions by system • of weighting. Also weight uncertainty (e.g. of memory, • temporal discounting).Tyrrell (1993) = small negative activation Distance Night Prox Low Health 1.4 Dirtiness from Den = zero activation = small positive activation = positive activation Keep Sleep in Den Clean Reproduce = large positive activation (1.0) T T U U -0.02 -0.15 -0.25 -0.05 -0.05 Den -0.05 -0.02 in Sq -0.30 -0.10 -0.01 -0.04 -0.08 Mate Court Sleep Approach Explore For Mates Clean Approach Approach Leave Mate P. Den R. Den this Sq Explore P. Den R. Den P. Mate Rand. Dir All Dirs No Den in Sq Receptive Mate in Sq Courted Mate in Sq Den No Den in Sq in Sq N NE E SE S SW W NW Clean Sleep Mate Court Move Actions Extended Rosenblatt and Payton Free-Flow Hierarchy