Weak Slot and Filler Structures
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Weak Slot and Filler Structures Representation in a Semantic Net Frames can also be regarded as an extension to Semantic nets. Indeed it is not clear where the distinction between a semantic net and a frame ends. Semantic nets initially we used to represent labelled connections between objects. As tasks became more complex the representation needs to be more structured. The more structured the system it becomes more beneficial to use frames. A frame is a collection of attributes or slots and associated values that describe some real world entity. Frames on their own are not particularly helpful but frame systems are a powerful way of encoding information to support reasoning. Set theory provides a good basis for understanding frame systems. Each frame represents: a class (set), or an instance (an element of a class). Frame Knowledge Representation We have already met this type of structure when discussing inheritance in the last lecture. We will now study this in more detail.
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weak slot and filler
Slot and filler structures represent knowledge through attributes (slots) and their associated values (fillers). Weak slot and filler structures provide little domain knowledge. Frames are a type of weak structure where a frame contains slots describing an entity. Semantic networks also represent knowledge with nodes and labeled links, allowing inheritance of properties through generalization hierarchies. Both frames and semantic networks enable quick retrieval of attribute values and easy description of object relations, but semantic networks additionally allow representation of non-binary predicates and partitioned reasoning about quantified statements.
strong slot and filler
- Weak slot and filler structures for knowledge representation lack rules, while strong structures like Conceptual Dependency (CD) and scripts overcome this.
- CD represents knowledge as a graphical presentation of high-level events using symbols like actions, objects, modifiers. It facilitates inference and is language independent.
- Scripts represent commonly occurring experiences through structured sequences of roles, props, scenes, and results to predict related events. Both CD and scripts decompose knowledge into primitives for fewer inference rules.
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Semantic networks are a knowledge representation technique where concepts are represented as nodes in a graph, and relationships between concepts are represented as links between nodes. There are different types of semantic networks, including definitional networks that emphasize subclass relationships, assertional networks for making propositions, and executable networks that can change based on operations. Common semantic relations include IS-A for subclasses, INSTANCE for examples, and HAS-PART for components. While semantic networks provide a natural representation of relationships, they have disadvantages like lack of standard link names and difficulty representing some logical constructs.
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- A sentence in first-order logic contains a predicate and a subject, represented by a variable. For example, "tall(John)" asserts that John is tall. Quantifiers like "forall" and "exists" are used to structure sentences.
- First-order logic contains constants, variables, predicates, functions, connectives, equality, and quantifiers as its basic elements.
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weak slot and filler
Slot and filler structures represent knowledge through attributes (slots) and their associated values (fillers). Weak slot and filler structures provide little domain knowledge. Frames are a type of weak structure where a frame contains slots describing an entity. Semantic networks also represent knowledge with nodes and labeled links, allowing inheritance of properties through generalization hierarchies. Both frames and semantic networks enable quick retrieval of attribute values and easy description of object relations, but semantic networks additionally allow representation of non-binary predicates and partitioned reasoning about quantified statements.
strong slot and filler
- Weak slot and filler structures for knowledge representation lack rules, while strong structures like Conceptual Dependency (CD) and scripts overcome this.
- CD represents knowledge as a graphical presentation of high-level events using symbols like actions, objects, modifiers. It facilitates inference and is language independent.
- Scripts represent commonly occurring experiences through structured sequences of roles, props, scenes, and results to predict related events. Both CD and scripts decompose knowledge into primitives for fewer inference rules.
Semantic Networks
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This document provides an overview of first-order logic in artificial intelligence:
- First-order logic extends propositional logic by adding objects, relations, and functions to represent knowledge. Objects can include people and numbers, while relations include concepts like "brother of" and functions like "father of".
- A sentence in first-order logic contains a predicate and a subject, represented by a variable. For example, "tall(John)" asserts that John is tall. Quantifiers like "forall" and "exists" are used to structure sentences.
- First-order logic contains constants, variables, predicates, functions, connectives, equality, and quantifiers as its basic elements.
Dempster shafer theory
The Dempster-Shafer Theory was developed by Arthur Dempster in 1967 and Glenn Shafer in 1976 as an alternative to Bayesian probability. It allows one to combine evidence from different sources and obtain a degree of belief (or probability) for some event. The theory uses belief functions and plausibility functions to represent degrees of belief for various hypotheses given certain evidence. It was developed to describe ignorance and consider all possible outcomes, unlike Bayesian probability which only considers single evidence. An example is given of using the theory to determine the murderer in a room with 4 people where the lights went out.
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The document discusses fuzzy relations and fuzzy composition. It defines the fuzzy Cartesian product of two fuzzy sets as a fuzzy relation contained within the full Cartesian product space, with the membership function equal to the minimum of the membership values from each fuzzy set. Fuzzy composition is defined similarly to crisp composition, with max-min composition taking the maximum of the minimum membership grades and max-product composition taking the maximum of the product membership grades. An example calculates the fuzzy Cartesian product of two sample fuzzy sets and performs max-min and max-product composition on two example fuzzy relations.
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This document discusses weak slot-and-filler knowledge representation structures. It describes how slots represent attributes and fillers represent values. Semantic networks are provided as an example where nodes represent objects/values and links represent relationships. Property inheritance allows subclasses to inherit attributes from more general superclasses. Frames are also discussed as a type of weak structure where each frame contains slots and associated values describing an entity. The document notes challenges with tangled hierarchies and provides examples of how to resolve conflicts through inferential distance in the property inheritance algorithm.
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3) The document outlines different types of semantic networks including definitional, assertional, implicational, and learning networks. It also discusses advantages such as being a natural representation of knowledge, and disadvantages including lack of quantifiers and lack of intelligence.
Weak Slot and Filler Structure (by Mintoo Jakhmola LPU)
Weak slot and filler structures allow for quick retrieval of attribute values, easy description of relation properties, and embrace aspects of object-oriented programming. A slot is an attribute-value pair, and a filler is a value that a slot can take. Semantic nets represent concepts as nodes and relationships as labeled arcs, and the meaning of a concept comes from its relations to other concepts. Frames represent real-world entities as collections of attributes and values through a hierarchical class structure.
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The document provides an overview of knowledge representation formalisms including semantic networks and frames. It discusses the syntax and semantics of semantic networks, how they can represent relations between concepts using nodes and links, and techniques for inference including inheritance and intersection search. Frames are also introduced as another knowledge representation technique where information about concepts is organized into objects with attributes and values.
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This document discusses weak slot-and-filler knowledge representation structures. It describes how slots represent attributes and fillers represent values. Semantic networks are provided as an example where nodes represent objects/values and links represent relationships. Property inheritance allows subclasses to inherit attributes from more general superclasses. Frames are also discussed as a type of weak structure where each frame contains slots and associated values describing an entity. The document notes challenges with tangled hierarchies and provides examples of how to resolve conflicts through inferential distance in the property inheritance algorithm.
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Given a set of non-negative integers, and a value sum, determine if there is a subset of the given set with sum equal to given sum.
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An ensemble is itself a supervised learning algorithm, because it can be trained and then used to make predictions. The trained ensemble, therefore, represents a single hypothesis. This hypothesis, however, is not necessarily contained within the hypothesis space of the models from which it is built.
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2. Multilayer perceptrons can learn non-linear patterns by using multiple layers of perceptrons with weighted connections between them. They were developed to overcome limitations of single-layer perceptrons.
3. Perceptrons are trained using an error-correction learning rule called the delta rule or the least mean squares algorithm. Weights are adjusted to minimize the error between the actual and target outputs.
3.2 partitioning methods
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Here are the identities, states, and behaviors of the given objects:
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- 1. Topic:- Weak Slot-and-Filler Structures : Semantic Nets, Frames Name: Kushal Parikh Enrollment No. : 170950107049 Subject: Artificial Intelligence(AI) Department: CSE-A
- 2. Inheritable knowledge Player Height Weight Bats_throws John 6.1 180 Right_throws Sam 5.10 170 right_right Jack 6.2 215 Bats_throws •The relational knowledge base determines a set of attributes and associated values that together describe the objects of knowledge base. E.g. Player_info(“john”,”6.1”,180,right_throws) •The knowledge about the objects, their attributes and their values need not be as simple as shown. •One of the most powerful form of inference mechanisms is property inheritance.
- 3. Property Inheritance • Here elements of specific classes inherit attributes and values from more general classes in which they are included. • In order to support property inheritance objects must be organized into classes and classes must be arranged in generalization hierarchy.
- 4. Here, Lines==attributes and boxed nodes== object/values of attributesof an objects. person(Owen) instance(Owen, Person) team(Owen, Liverpool) isa has--part instance uniform colour team Liverpool Owen Red Nose Person Mammal This structure is also called as slot and filler structure. These structures are the devices to support property inheritance along isa and instance links.
- 5. Advantage of slot and filler structures: Monotonic reasoning can be performed more effectively than with pure logic and non monotonic reasoning is easily supported. Makes it easy to describe properties of relations. e.g. “does Owen has-part called nose?” Form of object oriented programming and has advantages such as modularity and ease of viewing by people.
- 6. Attribute= slot and its value= filler
- 7. Semantic nets In semantic nets information is represented as:– set of nodes connected to each other by a set of labelled arcs. Nodes represent: various objects / values of the attributes of object . Arcs represent: relationships among nodes. • In this network we could use inheritance to derive the additional info: has_part(jack, nose)
- 8. Partitioned semantic nets • Used to represent quantified expressions in semantic nets. • One way to do this is to partition the semantic net into a hierarchical set of spaces each of which corresponds to the scope of one or more variable. • “the dog bit the mail carrier” [partitioning not required]
- 9. “every dog has bitten a mail carrier”
- 10. “Every dog in the town has bitten the constable”
- 11. “Every dog in the town has bitten every constable”
- 12. What is Frames ? Frames can also be regarded as an extension to Semantic nets. Indeed it is not clear where the distinction between a semantic net and a frame ends. Semantic nets initially we used to represent labelled connections between objects. As tasks became more complex the representation needs to be more structured. The more structured the system it becomes more beneficial to use frames. A frame is a collection of attributes or slots and associated values that describe some real world entity. Frames on their own are not particularly helpful but frame systems are a powerful way of encoding information to support reasoning. Set theory provides a good basis for understanding frame systems. Each frame represents: • a class (set), or • an instance (an element of a class).
- 13. Frames • Another kind of week slot and filler structure. • Frame is a collection of attributes called as slots and associated values that describe some entity in the world (filler). Consider,
- 14. Frame system for Hotel Room:
- 15. Frame Structure of Baseball Team:
- 16. Thank You