'Closed world assumption' presentation slideshows

Data Mining Chapter 2 Input: Concepts, Instances, and Attributes

Data Mining Chapter 2 Input: Concepts, Instances, and Attributes. Kirk Scott. Hopefully the idea of instances and attributes is clear Assuming there is something in the data to be mined, either this is the concept, or the concept is inherent in this

1.16k views • 92 slides

Data Mining Chapter 3 Output: Knowledge Representation

Data Mining Chapter 3 Output: Knowledge Representation. Kirk Scott. A summary of ways of representing knowledge, the results of mining: Rule sets Decision trees Regression equations Clusters Deciding what kind of output you want is the first step towards picking a mining algorithm.

1.03k views • 88 slides

LECTURE 4: PRACTICAL REASONING

864 views • 73 slides

Information Diffusion

Information Diffusion. Mary McGlohon CMU 10-802 3/23/10. Outline. Intro: Models for diffusion Epidemiological: SIS/SIR/SIRS Threshold models Case studies SIR: Info diffusion in blogs SIS: Cascades in blogs Timing: Cascades in chain letters A closer look: Network-based Marketing.

774 views • 55 slides

Towards Automatic Verification of Safety Architectures

556 views • 47 slides

Markov Logic Networks

Markov Logic Networks. Pedro Domingos Dept. Computer Science & Eng. University of Washington (Joint work with Matt Richardson). Overview. Representation Inference Learning Applications. Markov Logic Networks. A logical KB is a set of hard constraints on the set of possible worlds

621 views • 44 slides

569 views • 43 slides

Part 7: Rules and Ontologies

Part 7: Rules and Ontologies. Combining rules and ontologies. We now know how to represent (possibly incomplete, evolving, etc) knowledge using rules, but assuming that the ontology is known. We also learned how to represent ontologies. The close the circle, we need to combine both.

520 views • 40 slides

CS 9010: Semantic Web

CS 9010: Semantic Web. Inference and Rules Paula Matuszek Spring, 2006. Beyond Ontologies. With ontologies we can represent a lot of knowledge about the world: Classes of objects Instances of those classes Properties of those classes, including Relationships among those classes:

542 views • 36 slides

CS 4705: Semantic Analysis: Syntax-Driven Semantics

CS 4705: Semantic Analysis: Syntax-Driven Semantics. Slides adapted from Julia Hirschberg. Today. Reading: Ch 17.2-17.4, 18.1-18.7 (cover material through today); Ch 17.1-17.5 (next time) First Order Predicate Calculus as a representation Semantic Analysis: translation from syntax to FOPC.

650 views • 33 slides

Semantics: Representations and Analyses

Semantics: Representations and Analyses. Slides adapted from Julia Hirschberg, Dan Jurafsky, Chris Manning. Question-Answering/Dialog. Where does the information come from?. One possibility: http://newyork.citysearch.com. NL Architecture. Syntax. Morphology. Semantics.

446 views • 32 slides

For Wednesday

For Wednesday. Read chapter 10 Prolog Handout 4. Exam 1. Monday Take home due at the exam. Try It. reverse(List,ReversedList) evenlength(List) oddlength(List). The Anonymous Variable. Some variables only appear once in a rule Have no relationship with anything else

425 views • 32 slides

KNOWLEDGE REPRESENTATION, REASONING AND DECLARATIVE PROBLEM SOLVING

KNOWLEDGE REPRESENTATION, REASONING AND DECLARATIVE PROBLEM SOLVING. Chitta Baral Arizona State University Tempe, AZ 85287. Introduction. Long term goal of this line of research: Make programs, systems, interfaces and agents behave intelligently. What is intelligence?.

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Introduction to Artificial Intelligence LECTURE 11 : Nonmonotonic Reasoning

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For Wednesday

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First-Order Logic: Review

First-Order Logic: Review. First-order logic. First-order logic (FOL) models the world in terms of Objects, which are things with individual identities Properties of objects that distinguish them from others Relations that hold among sets of objects

534 views • 26 slides

Logic Programming

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The ancestor problem

The ancestor problem . For the ancestor problem we have the following rules in AnsProlog: anc(X,Y)<- par(X,Y). anc(X,Y)<- par(X,Z), anc(Z,Y). par(a,b). par(b,c). par(d,c). These rules work for the Closed World Assumption. Ancestor problem (contd.).

489 views • 18 slides