Complete Presentation on Mycin - An Expert System. ,mycin - an expert system ,mycin ,mycin expert system ,mycin system ,mycin expert ,expert system mycin ,mycin presentation ,how mycin work ,mycin architecture ,components of mycin ,tasks of mycin ,how mycin became successful ,is mycin used today? ,user interface of mycin
Complete Presentation on Mycin - An Expert System. ,mycin - an expert system ,mycin ,mycin expert system ,mycin system ,mycin expert ,expert system mycin ,mycin presentation ,how mycin work ,mycin architecture ,components of mycin ,tasks of mycin ,how mycin became successful ,is mycin used today? ,user interface of mycin
This presentation is an introduction to artificial intelligence: knowledge engineering. Topics covered are the following: knowledge engineering, requirements of expert systems (ES), functional requirements of ES, structural requirements of ES, components of ES/KBS, knowledge base, inference engine, working memory, expert system, explanation facility, user interface, will ES work for my problem.
Knowledge representation In Artificial IntelligenceRamla Sheikh
facts, information, and skills acquired through experience or education; the theoretical or practical understanding of a subject.
Knowledge = information + rules
EXAMPLE
Doctors, managers.
In this tutorial, we will learn the the following topics -
+ Voting Classifiers
+ Bagging and Pasting
+ Random Patches and Random Subspaces
+ Random Forests
+ Boosting
+ Stacking
Machine Learning Ml Overview Algorithms Use Cases And ApplicationsSlideTeam
"You can download this product from SlideTeam.net"
Machine Learning ML Overview Algorithms Use Cases and Applications is for the mid level managers giving information about Machine Learning, how Machine Learning works, Machine Learning algorithms and its use cases. You can also learn the difference between Machine learning vs Traditional programming to understand how to implement machine learning in a better way for business growth. https://bit.ly/2ZaVSG9
Supervised vs Unsupervised vs Reinforcement Learning | EdurekaEdureka!
YouTube: https://youtu.be/xtOg44r6dsE
(** Python Data Science Training: https://www.edureka.co/python **)
In this PPT on Supervised vs Unsupervised vs Reinforcement learning, we’ll be discussing the types of machine learning and we’ll differentiate them based on a few key parameters. The following topics are covered in this session:
1. Introduction to Machine Learning
2. Types of Machine Learning
3. Supervised vs Unsupervised vs Reinforcement learning
4. Use Cases
Python Training Playlist: https://goo.gl/Na1p9G
Python Blog Series: https://bit.ly/2RVzcVE
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Expert System - Automated Traffic Light Control Based on Road CongestionKartik Shenoy
This provides a summary of the aforementioned Expert System as referred from few reference papers cited at the end. It describes the summary of the modules of this expert system and the technique used behind them.
This presentation is an introduction to artificial intelligence: knowledge engineering. Topics covered are the following: knowledge engineering, requirements of expert systems (ES), functional requirements of ES, structural requirements of ES, components of ES/KBS, knowledge base, inference engine, working memory, expert system, explanation facility, user interface, will ES work for my problem.
Knowledge representation In Artificial IntelligenceRamla Sheikh
facts, information, and skills acquired through experience or education; the theoretical or practical understanding of a subject.
Knowledge = information + rules
EXAMPLE
Doctors, managers.
In this tutorial, we will learn the the following topics -
+ Voting Classifiers
+ Bagging and Pasting
+ Random Patches and Random Subspaces
+ Random Forests
+ Boosting
+ Stacking
Machine Learning Ml Overview Algorithms Use Cases And ApplicationsSlideTeam
"You can download this product from SlideTeam.net"
Machine Learning ML Overview Algorithms Use Cases and Applications is for the mid level managers giving information about Machine Learning, how Machine Learning works, Machine Learning algorithms and its use cases. You can also learn the difference between Machine learning vs Traditional programming to understand how to implement machine learning in a better way for business growth. https://bit.ly/2ZaVSG9
Supervised vs Unsupervised vs Reinforcement Learning | EdurekaEdureka!
YouTube: https://youtu.be/xtOg44r6dsE
(** Python Data Science Training: https://www.edureka.co/python **)
In this PPT on Supervised vs Unsupervised vs Reinforcement learning, we’ll be discussing the types of machine learning and we’ll differentiate them based on a few key parameters. The following topics are covered in this session:
1. Introduction to Machine Learning
2. Types of Machine Learning
3. Supervised vs Unsupervised vs Reinforcement learning
4. Use Cases
Python Training Playlist: https://goo.gl/Na1p9G
Python Blog Series: https://bit.ly/2RVzcVE
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Expert System - Automated Traffic Light Control Based on Road CongestionKartik Shenoy
This provides a summary of the aforementioned Expert System as referred from few reference papers cited at the end. It describes the summary of the modules of this expert system and the technique used behind them.
Basic student guide for learning CLIPS Expert System Language an Artificial Intelligent field,
This document used to teach Bachelor Student in the LAB of Computer Sciences in UQU.
Definition, architecture, general applications, and energy management specified application of expert systems - Class presentation - University of Tabriz 2019
BioAssay Express: Creating and exploiting assay metadataPhilip Cheung
The challenge of accurately characterizing bioassays is a real pain point for many drug discovery organizations. Research has shown that some organizations have legacy assay collections exceeding 20,000 protocols, the great majority of which are not accurately characterized. This problem is compounded by the fact that many new protocol registrations are still not following FAIR (Findability, Accessibility, Interoperability, and Reusability) Data principles.
BioAssay Express is a tool focused on transforming the traditional protocol description from an unstructured free form text into a well-curated data store based upon FAIR Data principles. By using well-defined annotations for assays, the tool enables precise ontology based searches without having to resort to imprecise keyword searches.
This talk explores a number of new important features designed to help scientists accelerate the drug discovery process. Some example use-cases include: enabling drug repositioning projects; improving SAR models; identifying appropriate machine learning data sets; fine-tuning integrative-omic pathways;
An aspirational goal for our team is to build a metadata schema based on semantic web vocabularies that is comprehensive to the extent that the text description becomes optional. One of the many possibilities is to take the initial prospective ELN entry for a bioassay protocol and feed it directly to an automated instrument. While there are many challenges involved in creating the ELN-to-robot loop, we will provide some insights into our collaborations with UCSF automation experts.
In summary, the ability to quickly and accurately search or analyze bioassay data (public or internal) is a rate limiting problem in drug discovery. We will present the latest developments toward removing this bottleneck.
https://plan.core-apps.com/acs_sd2019/abstract/6f58993d-a716-49ad-9b09-609edde5a3f4
How to Use Open Source Technologies in Safety-critical Digital Health Applica...Shahid Shah
Presented at 3rd Annual Open Source EHR Summit - Key Takeaways:
* Outcomes driven care (vs. fees for service or volume driven care) is in our future
* Because outcomes now matter more than ever, open source digital health solutions are even more important
* There are new realities of patient populations driving open source even faster
* How to use open source reliably and and securely in a safety-critical environment like medical devices
Application of Expert Systems inSystem Analysis & Designfaiza nahin
Design is a field in which a large part of the processes involved is knowledge-based rather than computation-based. Much of this knowledge is experiential and as such lends itself to be encapsulated in an expert system. An analogy is made between analysis and interpretation and between evaluation and comparison of interpretations. Three examples of expert systems carrying out design analysis and evaluation in different domains are described. It is argued that a graphical interface and a model of the elements within the domain are essential parts of any design system.
Maximize Your Understanding of Operational Realities in Manufacturing with Pr...Bigfinite
Maximize Your Understanding of Operational Realities in Manufacturing with Predictive Insights using Big Data, Artificial Intelligence, and Pharma 4.0
by Toni Manzano, PhD, Co-founder and CSO, Bigfinite
PDA Annual Meeting 2020
In a Power plant with a Distributed Control System ( DCS ), process parameters are continuously stored in databases at discrete intervals. The data contained in these databases
may not appear to contain valuable relational information but practically such a relation exists.
The large number of process parameter values are changing with time in a Power Plant. These parameters are part of rules framed by domain experts for the expert system. With the changes in parameters there is a quite high possibility to form new rules using the dynamics of the process itself. We present an efficient algorithm that generates all significant rules based on the real data. The association based algorithms were compared and the best suited algorithm for this process application was selected. The application for the Learning system is studied in a Power Plant domain. The SCADA interface was developed to acquire online plant data
Similar to Introduction To Mycin Expert System (20)
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Neuro-symbolic is not enough, we need neuro-*semantic*
Introduction To Mycin Expert System
1. PRESENTED BY NIPUN JASWAL
MYCIN was an early expert
system that used artificial
intelligence to identify
bacteria causing severe
infections, such as
bacteremia and meningitis,
and to
recommend antibiotics, with
the dosage adjusted for
patient's body weight — the
name derived from the
antibiotics themselves, as
many antibiotics have the
suffix "-mycin". The Mycin
system was also used for
the diagnosis of blood
clotting diseases.
MYCIN was never actually
used in practice. This wasn't
because of any weakness in its
performance. As mentioned, in
tests it outperformed members
of the Stanford medical school
faculty. Some observers raised
ethical and legal issues related
to the use of computers in
medicine — if a program gives
the wrong diagnosis or
recommends the wrong
therapy, who should be held
responsible? However, the
greatest problem, and the
reason that MYCIN was not
used in routine practice, was
the state of technologies for
system integration, especially
at the time it was developed.
MYCIN EXPERT SYSTEM
www.nipunjaswal.com How Helpful Are Expert Systems In Medical ? 20, Apr 2013
2. Your Logo
What is to be Covered?
History and Overview
MYCIN Architecture
Consultation System
Explanation System
Knowledge Acquisition
Results, Conclusions
TOPIC : MYCIN E.S
3. Your Logo
History
Thesis Project by Shortliffe from Stanford
Davis, Buchanan, van Melle, and others
Stanford Heuristic Programming Project
Infectious Disease Group, Stanford Medical
Project Spans a Decade
Research started in 1972
Original implementation completed 1976
Research continues into the 80’s as well
TOPIC : MYCIN E.S
4. Your Logo
Why Mycin ?
Disease DIAGNOSIS and Therapy SELECTION
Advice for non-expert physicians with time considerations and incomplete
evidence on:
Bacterial infections of the blood
Expanded to other ailments
TOPIC : MYCIN E.S
5. Your Logo
System Goals
Utility
Be useful, to attract assistance of experts
Demonstrate competence
Flexibility
Domain is complex, variety of knowledge types
Medical knowledge rapidly evolves, must be easy to maintain K.B.
TOPIC : MYCIN E.S
6. Your Logo
System Goals
Interactive Dialogue
Provide easy explanations
Allow for real-time K.B. updates by experts
Fast and Easy
Meet time constraints of the medical field
6TOPIC : MYCIN E.S
8. Your Logo
Consultation System Performs Diagnosis and
Therapy Selection
Control Structure reads
Static DB (rules) and
read/writes to Dynamic DB
(patient, context)
Linked to Explanations
Terminal interface to
Physician
8TOPIC : MYCIN E.S
9. Your Logo
Consultation System
User-Friendly Features:
Users can request rephrasing of questions
Synonym dictionary allows latitude of user responses
Questions are asked when more data is needed
If data cannot be provided, system ignores relevant rules
9TOPIC : MYCIN E.S
10. Your Logo
Consultation “Control Structure”
High-level Algorithm:
1. Determine if Patient has significant infection
2. Determine likely identity of significant organisms
3. Decide which drugs are potentially useful
4. Select best drug or coverage of drugs
TOPIC : MYCIN E.S
11. Your Logo
Static Database Rules
Meta-Rules
Templates
Rule Properties
Context Properties
Fed from Knowledge
Acquisition System
11TOPIC : MYCIN E.S
12. Your Logo
Production Rules
Represent Domain-specific Knowledge
Over 450 rules in MYCIN
Premise-Action (If-Then) Form
Each rule is completely modular, all relevant context is contained in the rule
with explicitly stated premises
12TOPIC : MYCIN E.S
13. Your Logo
MYCIN Assumptions
Not every domain can be represented, requires formalization
IF-THEN formalism is suitable for Expert Knowledge Acquisition and
Explanation sub-systems
13TOPIC : MYCIN E.S
14. Your Logo
Judgmental Knowledge
Inexact Reasoning with Certainty Factors (CF)
CF are not Probability!
Truth of a Hypothesis is measured by a sum of the CFs
Premises and Rules added together
Positive sum is confirming evidence
Negative sum is disconfirming evidence
14TOPIC : MYCIN E.S
15. Your Logo
Preview Mechanism
Interpreter reads rules before invoking them
Avoids unnecessary deductive work if the sub-goal has already been
tested/determined
Ensures self-referencing sub-goals do not enter recursive infinite loops
15TOPIC : MYCIN E.S
16. Your Logo
Meta-Rules
Alternative to exhaustive invocation of all rules
Strategy rules to suggest an approach for a given sub-goal
Ordering rules to try first, effectively pruning the search tree
Creates a search-space with embedded information on which branch is best
to take
16TOPIC : MYCIN E.S
17. Your Logo
Meta-Rules (continued)
High-order Meta-Rules (i.e. Meta-Rules for Meta-Rules)
Powerful, but used limitedly in practice
Impact to the Explanation System:
(+) Encode Knowledge formerly in the Control Structure
(-) Sometimes create “murky” explanations
17TOPIC : MYCIN E.S
18. Your Logo
Templates
The Production Rules are all based on Template structures
This aids Knowledge-base expansion, because the system can
“understand” its own representations
Templates are updated by the system when a new rule is entered
18TOPIC : MYCIN E.S
19. Your Logo
Dynamic Database Patient Data
Laboratory Data
Context Tree
Built by Consultation
System
Used by Explanation
System
19TOPIC : MYCIN E.S
20. Your Logo
Therapy Selection
Plan-Generate-and-Test Process
Therapy List Creation
Set of specific rules recommend treatments based on the probability
(not CF) of organism sensitivity
Probabilities based on laboratory data
One therapy rule for every organism
20TOPIC : MYCIN E.S
21. Your Logo
Therapy Selection
Assigning Item Numbers
Only hypothesis with organisms deemed “significantly likely” (CF) are
considered
Then the most likely (CF) identity of the organisms themselves are
determined and assigned an Item Number
Each item is assigned a probability of likelihood and probability of
sensitivity to drug
21TOPIC : MYCIN E.S
22. Your Logo
Therapy Selection
Final Selection based on:
Sensitivity
Contraindication Screening
Using the minimal number of drugs and maximizing the coverage of
organisms
Experts can ask for alternate treatments
Therapy selection is repeated with previously recommended drugs
removed from the list
22TOPIC : MYCIN E.S
23. Your Logo
Explanation System
Provides reasoning
why a conclusion has
been made, or why a
question is being
asked
Q-A Module
Reasoning Status
Checker
23TOPIC : MYCIN E.S
24. Your Logo
Explanation System
Uses a trace of the Production Rules for a basis, and the Context Tree, to
provide context
Ignores Definitional Rules (CF == 1)
Two Modules
Q-A Module
Reasoning Status Checker
24TOPIC : MYCIN E.S
25. Your Logo
Reasoning Status Checker
Explanation is a tree traversal of the traced rules:
WHY – moves up the tree
HOW – moves down (possibly to untried areas)
Question is rephrased, and the rule being applied is explained with the
translation patterns
25TOPIC : MYCIN E.S
26. Your Logo
Knowledge Acquisition System
Extends Static DB via
Dialogue with Experts
Dialogue Driven by
System
Requires minimal
training for Experts
Allows for Incremental
Competence, NOT an
All-or-Nothing model
26TOPIC : MYCIN E.S
27. Your Logo
Knowledge Acquisition
IF-THEN Symbolic logic was found to be easy for experts to learn, and
required little training by the MYCIN team
When faced with a rule, the expert must either except it or be forced to
update it using the education process
27TOPIC : MYCIN E.S
28. Your Logo
Results
Never implemented for routine clinical use
Shown to be competent by panels of experts, even in cases where experts
themselves disagreed on conclusions
Key Contributions:
Reuse of Production Rules (explanation, knowledge acquisition
models)
Meta-Level Knowledge Use
28TOPIC : MYCIN E.S