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pervasive computing > Chapter 4 [ C ] > Flashcards

Chapter 4 [ C ] Flashcards

1
Q

Describe a Learning-based IS (L-IS).

A

Learning refers to system improving its performance with experience, with respect to some task.

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2
Q

When is a System said to learn from experience E with respect to some class of actions A and performance measure P?

A
  • if its performance at the set of actions A, as measured by P, improves with experience E.
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3
Q

Describe an Adaptive transport scheduling example for L-IS.

A
  • A = “a logistics vehicle picks up goods on route”,
  • E = “traveling the route”,
  • P = “deviation of actual time from predicted time”.

-> Improvement is measure P reducing to zero.

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4
Q

In L-IS Design of the learning element depends on?

A
  • Which model is learned,
  • The type of feedback and the model or knowledge representation.
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5
Q

What are the 3 main types of learning or feedback which can be used?

A
  • supervised learning,
  • unsupervised learning
  • reinforcement learning
    [ since Learning may need model representations that can handle uncertainty ]
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6
Q

What are the commonly used types of knowledge representations?

A
  1. Blackboard / EDA systems
  2. Production / rule-based systems
  3. Syntactical: RDBMS, XML Web services
  4. Semantic Type KBs (Ontology-based Systems)
  5. Classic-Logic-Based KBs
  6. Soft Computing
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7
Q

In a production system, knowledge is represented as _____ ?

A

a set of rules or productions

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8
Q

Rules are typically defined as ______ ?

A

IF-fact | THEN-fact

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9
Q

IF-fact is aka?

A
  • Condition / Antecedent part
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10
Q

THEN-fact is aka?

A
  • Action / Consequence part
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11
Q

Rule-based systems use a set of predefined rules to make decisions or perform actions based on input data.

A

True

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12
Q

Give some examples of rule-based engines fragments for the other 3 scenarios:
1. Personal memories
2. Foodstuff management
3. Utility Regulation

A
  1. Personal memories
    Rule: If today’s date matches a stored event date, trigger the memory recall. [Snapchat]
  2. Foodstuff management
    Rule: If the current date is within a week of the expiration date of a stored food item, send a notification.
  3. Utility Regulation
    Rule: If energy consumption exceeds a certain threshold during peak hours, send an alert and suggest energy-saving measures.
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13
Q

How can Rules get added to the KB Systems?

A
  • Manually [ e.g: enterprise policy-based systems ]
  • Automatically [ e.g : machine learning ]
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14
Q

________ enable rule-based systems to incorporated as part of more general distributed systems versus as part of more specialiszed IS?

A

rule-based engines

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15
Q

Describe a Blackboard (BB) system.

A
  • is a collaborative knowledge-based approach where multiple knowledge sources contribute to solving complex problems.
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16
Q

Knowledge sources of a BB can be?

A
  • Independent & distributed
  • Heterogeneous
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17
Q

Give an example of Ontology using Vehicle.

A
  1. Concepts:
    - Vehicle: Represents the general concept of a vehicle.
    - Car: A specific type of vehicle.
  2. Relationships:
    - “is-a” Relationship: Car is-a Vehicle
    - “has-a” Relationship: Car has-a Engine.
  3. Properties:
    - Color Property: Vehicle has Color, Car has Color.
    - Fuel Type Property: Vehicle has Fuel Type, Car has Fuel Type.
  4. Constraints:
    - If a vehicle is a Car, it must have exactly four wheels.
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18
Q

Give a formal and informal definition of ontology.

A
  • Informally : a collection of descriptions of the world that helps us to define the meaning of their actions on the world.
  • Formally : A formal, explicit specifications of a shared conceptualization
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19
Q

What is the r/n ship b/n Ontology and Semantic (IS) : Knowledge Representation (KR)?

A
  • Ontology based models support semantic conceptualization & can directly support reasoning
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20
Q

______ is often used synonymously with the term semantic KR?

A

Ontology

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21
Q

Depending on how concepts and their relationships are defined and organized what are the ranges of Knowledge Representation (KR) [ or Ontology ]?

A
  • Light-weight
  • Medium-weight
  • Heavy-weight
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22
Q

Describe what a Light-weight Semantic Representation is.

A
  • have a simple conceptualization with simple structures.
  • representations may not be highly machine-readable or easily relatable to other terms.
  • Example: Dictionaries
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23
Q

The most widely used light-weight KRs are based on _____ ?

A

W3C Web XML standards.

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24
Q

Explain W3C Web XML standards?

A
  • Defines an unnamed hierarchy of concepts & properties
  • Acts as a basic node labelled graph representation

Example:

<ontology>

<concept>
<property>
<value> Four </value>
</property>
</concept>

</ontology>

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25
Q

_____ is a language designed for exchanging extensible application specific hierarchical data structures?

A

XML

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26
Q

XML extensions Is used for _____ ?

A

SOC

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27
Q

XML is an easy data format on which to build automated machine-understandable processing.

A

False.
- It is a difficult data format

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28
Q

_____ & _____ represent Node Labelled
Graph & Edge Labelled Graph respectivley?

A

Light-Weight KR & Medium-Weight KR

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29
Q

Why was the Semantic Web (SW) created?

A
  • to evolve the Web from machine-readable to machine-understandable
  • to support richer service interoperability
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30
Q

What are the suit of KRs defined by SW and their weights?

A
  1. RDF (Resource Description Framework):
    Weight: Medium to Heavy
  2. RDFS (RDF Schema):
    Weight: Medium to Heavy
  3. OWL (Web Ontology Language):
    Weight: Heavy
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31
Q

What are the Semantic KR: Design Issues?

A
  • Open World versus Closed World Semantics
  • Knowledge Life-cycle and Knowledge Management
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32
Q

Compare Open World versus Closed World Semantics.

A
  • Open world : knowledge about a domain is incomplete, and new information may be discovered over time.
  • Closed world : knowledge about a domain is complete and known. [ If not T then F ]
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33
Q

What aspects are involved in Knowledge Life-cycle and Knowledge Management?

A
  • Creating Knowledge
  • Knowledge Deployment
  • Maintaining Knowledge
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34
Q

_____ is expressed by data is not present, it is false (negation as failure)?

A

Closed World Semantics

35
Q

RDBMS type KB models assume _______ semantics?

A

closed-world

36
Q

Semantic type KBs tend to use ______ semantics.

A

open-world

37
Q

______ regard absence as unknown (negation as unknown)?

A

Open World Semantics

38
Q

Semantics of data is often defined to be declarative.

A

False.
- is dynamic, heterogeneous and depends on context

39
Q

Semantics is undefined or variable.

A

True

40
Q

_______ enable less expert developers & users to create knowledge models.

A

interactive knowledge creation tools

41
Q

What is a key feature of KB IS: Creation Tools?

A

the ability to export knowledge models in a format that can be easily interpreted, parsed, and invoked by computer applications, often through APIs

42
Q

Give examples of KB IS: Creation Tools.

A
  • Protégé
  • Jena
43
Q

Devices embedded into a local environment often have _______ of their environment.

A

partial view [ rather than a global view]

44
Q

________ is positioned at the core of information systems that need to support reasoning about the system?

A

Classic Logic, based on first-order predicate logic

45
Q

_____ systems involve the manipulation of
symbols in the form of logic formulae?

A

Classic Logic Systems

[ logic reasoning systems
[ deliberative IS
[ symbolic Al

46
Q

What is Propositional logic?

A

knowledge represented in the form of relations which are either true or false.

47
Q

What are The standard logic operators?

A
  • And, or, not, equals, implies
48
Q

Describe a Predicate logic.

A

supports more expressive sentences than propositions by allowing properties to be related to objects or values.

49
Q

Give examples for Predicate logic and Propositional Logic.

A
  1. P: It is raining.
    Q: I am staying indoors.
    P⇒Q: If it is raining, then I am staying indoors.
  2. Sentence: “Device A is in hibernate mode.”
    Predicate: mode ( Device A , Hibernate )
    - Here, the predicate relates the object “Device A” to the property “Hibernate mode.”
50
Q

_____ is the Most c o m m o n form of Predicate logic?

A

First-Order Predicate Logic [ FOPL ]

51
Q

What is Description Logic (DL)?

A
  • Logic based on combining First-Order Logic (FOL) with a conceptualization on graphs [ i.e: RDF Schema (RDF-S) … ]
52
Q

______ is Used extensively in Heavy-weight Ontology languages?

A

Description Logics (DL)

53
Q

Explain Reasoning?

A
  • Aka inferencing, involves logical operations on logical statements n order to draw conclusions.

E.g: A entails B, A |= B.

54
Q

Inferencing is used to search for entailments.

A

True

55
Q

______ is used to check that the entailments of sentences are valid in all possible worlds or models.

A

Model Checking

56
Q

What are Tautologies?

A

Sentences that are valid in all possible worlds

57
Q

What are the design issues of Reasoning?

A

Reasoning needs to be :
* scalable
* selectively used
* computationally efficient

58
Q

What are the Challenges of Classic Logic Systems?

A
  1. Difficulty in expressing exceptions
  2. Imprecision
  3. Uncertainty
  4. High computation is needed to establish truth
  5. Logical inconsistencies
  6. Existence of Different sub-types and extensions to classical logic
59
Q

Many decisions which involve interaction with humans and the physical world are soft.

A

True
- rather than being expressed as either true or false

60
Q

What is Soft computing?

A
  • is a computing paradigm that deals with problems that are inherently imprecise and uncertain, often involving human judgment
61
Q

_____ is also called a Belief network or Bayesian Network (BN)?

A

Probabilistic network

62
Q

What are Probabilistic networks?

A
  • networks used to model the likelihood of events that are uncertain in a proposition or predicate
63
Q

_______ consider a prior or unconditional probability and conditional or posterior probabilities.

A

Probabilistic networks

64
Q

What is the Product Law?

A
  • Expresses a conditional probability in terms of another conditional probability and two unconditional probabilities.
65
Q

What is Fuzzy Logic?

A

is a mathematical framework that deals with imprecision and approximation

66
Q

Explain Fuzzy Logic Application in the Adaptive
Transport Scheduling Scenario.

A
  • If the bus is traveling slowly away from the pickup point
  • and a passenger is moving quickly towards the pickup point
  • then slow down the vehicle to stop near the pickup point.
  • Terms : slowly, quickly, and near, act as fuzzy
    descriptors.
67
Q

List the IS System Operations.

A
  • Searching
  • Planning
  • Reasoning
  • Learning
68
Q

_________ Is a problem-solving technique that systematically explores a space of problem statments.

A

Searching

69
Q

When searching, alternative solutions is searched to find an answer.

A

True

70
Q

How do we express a search problem?

A
  • Start state
  • Goal state
  • Goal test function [ is it goal state or nah ]
  • Utility function [ is it desired or nah ]
71
Q

What is Uninformed search?

A
  • Also known as blind search, is a type of search algorithm that does not have any information about the structure or likelihood of success in the search space.
  • Checks every node
72
Q

What Traversal Strategies does Uninformed search algorithms typically use?

A
  • Breadth-first search [ BFS ]: Explores the search space level by level, starting from the root.
  • Depth-first search [ DFS ]: Explores as far as possible along one branch before backtracking.
73
Q

Uninformed problem space searches tend to operate in the ______ direction?

A

forward
- Forward-chaining:

74
Q

What are the Challenges in Uninformed Search Algorithm Design?

A
  1. Handling Unknown Start State: the start state maybe unknown
  2. Non-Uniform Search Space: different nodes may have varying numbers of branches
  3. Multiple-Valued Problem Spaces: multiple possible solutions or paths
  4. Large Search Spaces
75
Q

_____ is a general solution to reduce the
computation of an uninformed search?

A

Informed search

76
Q

_______ Uses problem specific information to limit the problem space?

A

Informed search

77
Q

What is the core component of an informed search?

A
  • A heuristic function
78
Q

What is A heuristic function?

A
  • Is a function that depends on the current node in the problem space.
  • Guides the search algorithm by prioritizing paths that seem more promising based on the estimated cost.
79
Q

What is A* search?

A

a popular informed search algorithm

80
Q

What is Planning?

A

Planning involves searching for a plan and then executing the plan.

81
Q

What is represented by the Planning model?

A
  • States
  • Goals
  • Actions : which transition states towards goals
  • Chains of actions : between non-adjacent states
  • Heuristic cost function : to allow the choice of multiple paths
82
Q

When planning is modeled as a graph what do the nodes and the link between nodes represent?

A
  • nodes : represent states
  • link between nodes : represent actions
83
Q

Explain The integration of planning and Geographic Information Systems (G-IS) design.

A
  • It involves using planning to enable the selection of a chain of actions that will lead to the achievement of a goal within a geographic context /Environment

pervasive computing (3 decks)

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