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UbiCom Book Slides
Chapter 8
Intelligent Systems
(Part A: Basics)
Stefan Poslad
http://www.eecs.qmul.ac.uk/people/stefan/ubicom
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Chapter 8: Overview
Chapter 8 focuses on:
• Internal system properties: intelligence
• External interaction with any of three types of environment
– Focussing more on ICT and physical environment
– These environments tend to be passive
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Five main properties for UbiCom
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Chapter 8: Overview
The slides for this chapter in the full pact are expanded and
split into several parts
• Part A: Basics 
• Part B: R-IS, EM-IS
• Part C: G-IS, U-IS & H-IS Models
• Part D: KB IS Models
• Part E: KB Acquisition
• Part F: KB Representation: Rule-Based, BB
• Part G: KB Representation: Semantic
• Part H: Classic Logic KB Models
• Part I: Soft Computing KB Models
• Part I: Generic IS Operations
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Related Chapter Links
• There are two AI chapters that are interlinked
• This chapter. 8, describes the design of single Intelligent
System or IS
– These may be simple: use a single models of intelligence
– These may be hybrid: use multiple heterogeneous intelligence
models
• Chapter 9, describes the interaction between multiple ISs
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Related Chapter Links
• Many AI researchers see autonomy as a sub-type of
intelligence.
– Sometimes their notion of autonomy is not well-defined
• In this text we separately autonomy and intelligence as
main concepts – both as main types of property for UbiCom
• A very rich model for autonomy is given in Chapter 10
– Relates the UbiCom properties of intelligence & autonomy
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Related Chapter Links
• Reflexive Intelligent system models are related to:
–
–
–
–
Event-based system models (Chapter 3)
Sensor systems for the physical Environment (Chapter 6)
Controller systems for the physical Environment (Chapter 6)
Context-aware system design (Chapter 7)
• Hybrid Goal-based environment model:
– Based upon used of EDA, BB, pipe-filter interaction design (Chapter
3)
– Used in context-aware system design (Chapter 7)
– Used in autonomic computing designs (Chapter 10)
• Monitoring & analysis of self rather than one’s environment
– Self-awareness & Reflection (Chapter 10)
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Overview
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Types of Intelligence and IS Model 
Reflex & Environment Models for IS
Goal and Utility based models for IS
Hybrids IS Models
KB IS Models
KB Management: Creation & Deployment
Knowledge Representations: rules, BB, semantics
Logic reasoning Models for IS
Soft Computing IS
Generic IS Operations
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Intelligent systems (IS): Introduction
• Systems that use AI algorithms.
• Also called
– Machine intelligence or computational intelligence
– Agent-based systems
• AI effect
???
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IS Introduction
• IS Models often use analogies of human problem solving
• IS models can be based upon physical organisations
• But Machines may not problem solve like humans
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Human Intelligence vs. Machine
Intelligence
• Compare & Contrast
• What complex tasks are humans good at and machines
less so and vice versa?
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Types of Intelligence
Can we define intelligence as any single concept in any single
definition? Or is intelligence a multi-dimensional concept
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Intelligent Systems (IS)
IS vs. distributed system?
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UbiCom Systems that use an implicit
notion of Intelligence
Usage of term AI in general computing varies
• HCI
• Sensor / Context-aware systems
• Control system & Robots
• Intelligent networks
• Network, e.g., SNMP, Agents
• AmI
• Smart devices
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Types of IS Model
IS models can be classified in terms of:
• Type of Model / Architecture
• How a model is used to solve some problem
• What is being modelled
• What types of environment, a system is situated in, and
can operate in
• How are the models in an IS acquired?
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Types of IS Model Representations
IS models can be classified in terms of:
Basic types of IS Representation
• Process-driven system models:
• Data-driven KB-IS models:
• Logic –based KB-IS models:
• Soft Computing models:
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Unilateral System Environment
Interaction Models
• Generally when ubiquitous system applications are
designed, a unilateral model of the environment is used.
Explain this?
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Unilateral versus Bilateral System
Environment Models
• Generally when ubiquitous system applications are
designed, a unilateral model of the environment is used,
– system models its environment, not vice-versa
• However, as we move to smarter environments
– Environments can be designed to contain a model of application
systems which are situated in them or pass through them
– Environment can be considered itself as an active, smart system
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Unilateral versus Bilateral System
Environment Models
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Bilateral System Environment Models
• A system that models an active environment
• And in turn, the active environment has a model of the
systems which use it
• Designers of systems may to take into the account the
degree of intelligence of environments
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IS Environment Types
• It is a challenge for any system to act in open system
environments.
• Russell and Norvig (2003) have categorised open system
environments along several dimensions .
• Simplest types of system environments are those that are
fully-observable, episodic and static.
• More complex designs for intelligent systems are needed to
think and act in environments that: are uncertain and nondeterministic etc.
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IS System Environment Types
Environment Description of Environment
Fully
observable
Deterministic
Episodic
Static
Discrete
Passive
Antonym
UbiCom’s sensors give it access to complete state of Partially
environment at each point in time.
observable
Next state of environment determined by current state Stochastic
and action executed by UbiCom system.
UbiCom choice of action depends only on current state Sequential
of environment on episode itself.
Environment static while UbiCom system selects & Dynamic
execute its actions i.e., to adapt to its environment
A limited number of distinct, clearly defined states and Continuous
actions characterise the environment
environment ii not active, in sense of modelling the Active
system that is acting in it.
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IS Environment Types
• Open system environment are often stochastic. Why?
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IS Environment Types
• Discuss examples of these System Environment Types:
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What is Modelled and How the Model
is Acquired
• What is Modelled?
• Different ways to acquire the model?
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When Models are Acquired: At Design
Time
Contrast acquiring the model itself versus acquiring the
content to populate the model
2 main ways to design how the model a acquired?
• Models can be built into system at design time & modified
at run-time
• Models can be built at design time so that they can modify
themselves at run-time
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When Models are Acquired
• Models can be created by a human designer & built into
system at design time & upgraded later.
• What are benefits?
– ??
• What are the limitations?
– ??
• Systems/ environment can acquire their models
themselves, automatically
• Hybrid model acquisition systems
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Overview
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Types of Intelligence and IS Model
Reflex & Environment Models for IS 
Goal and Utility based models for IS
Hybrids IS Models
KB IS Models
KB Management: Creation & Deployment
Knowledge Representations: rules, BB, semantics
Logic reasoning Models for IS
Soft Computing IS
Generic IS Operations
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Should a UbiCom System be an IS?
• Depends upon the nature of the application.
• Depends upon the specific model of AI being used
• Specific IS models can be used to build systems which
support other UbiCom system properties such as contextawareness, autonomy and iHCI
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Basic Types of IS Model
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(From Russel & Norvig 2002)
Reflex Based
Environment Model based
Goal based, Proactive
Utility based
Learning
Multi-IS, Hybrid IS
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Basic Types of IS Model
Note also they can classify the types of IS model by the
knowledge representation they use for their model
• Rule-based
• Light-weight Ontology
• Heavy-weight Ontology
• Active user versus active service processes
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Types of IS Model & Types of
Environment They Suit
Type of
Model
IS What a IS’s actions Types of environments IS design is
depend upon
suited to
Reflex Based
Current Environment
context
Env. Model
Current and past
based, Situated environment context
action
Goal based,
IS’s plans of actions
Proactive
to achieve a goal
Utility based IS’s weighting of
different goals and
plans.
Learning
Performance
Multi-IS,
Hybrid IS
Fully-observable, stochastic, episodic,
static, physical
Partially-observable, deterministic,
sequential, dynamic
Partially-observable, deterministic,
sequential, dynamic human
Partially-observable, Semideterministic, sequential, dynamic
Partially-observable, Nondeterministic, sequential, dynamic
Partially-observable, Nondeterministic, sequential, dynamic
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Reactive IS Models (R-IS)
• Intelligent behaviour arises out of system’s interaction with
environment rather than as result of complex internal
knowledge representation or reasoning about events.
• Action selection is at heart of the intelligent system.
– in the simple case is driven by current state of environment.
• R-IS is strongly situated in its environment and is highly
responsive to changes in the environment.
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Pure versus Hybrid R-IS Models
• R-IS tend to be designed as event-based systems
–
• Pure reactive type of IS works best when?
–
• In practice many systems are designed not to be purely
reactive
–
• These represent hybrid reactive systems.
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(Pure) R-IS Model
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R-IS Design: Present action Rulebased actions
• Preset actions may be directly triggered from sensor input
without any conditions
• Alternatively events can be filtered by conditions / rules in
order to trigger actions
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R-IS Design: handling multiple
concurrent & heterogeneous events
3 possible designs:
• Discard events
• Event persistence
• Concurrent Event Handling
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R-IS Design: handling multiple
concurrent & heterogeneous events
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UbiCom Systems based upon R-IS
• R-IS design is good design for minimum context-aware CA) system
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Environment Model based IS (EM-IS)
• EM-IS ~ KB system: knowledge about world & its actions.
• EM-IS models historical behaviour of its environment.
• System environment may be partially observable. Why?
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EM-IS
• How to handle partial observability of environment?
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EM-IS
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EM-IS
• IS’s actions depend upon current environment state, past
environment states & on knowing effect of system actions
• Similar to a situated action type of system design:
– actions can be unplanned and depend strongly on context
• Can anticipate multiple future environment states, which
may never be realised leading to a theory of multiple
possible future environments or world states.
• EM-IS not include a model of the internal behaviour, e.g.,
processes of actions by the system
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EM-IS
• Systems that build such a model of the environment enable
their services & applications to optimise & adapt their
behaviour to account for behaviours in environment which
are not accessible but which are predetermined (as defined
in the environment model).
• E.g.,, in the adaptive transport scheduling scenario,
– In pure R-IS, vehicle will not stop when no passengers are at
pickup point (providing no passengers on the vehicle wish to leave).
– EM-IS: …..
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Overview
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•
•
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Types of Intelligence and IS Model
Reflex & Environment Models for IS
Goal and Utility based models for IS 
Hybrid IS Models
KB-IS Models
KB Management: Creation & Deployment
Knowledge Representations: rules, BB, semantics
Logic reasoning Models for IS
Soft Computing IS
Generic IS Operations
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Goal-based IS (G-IS)
• Also referred to as planning-based IS, defines an internal
plan or sequence of actions to achieve a future system goal
• Unlike EM-IS, action selection for a G-IS depends on which
next system action brings system towards future goal state
• G-IS tends to dissociate control of actions from
environment situation or context of action (unlike EM-IS)
• G-IS vs. R-IS, events which trigger system actions as
external events
• In G-IS, internal events, e.g., a scheduled system task that
becomes delayed, can also trigger system actions.
• Main benefit of G-IS: users can delegate tasks at a much
higher level of abstraction, focussing on what needs to be
achieved rather than on details of how this is achieved
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G-IS
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Utility-based IS (U-IS)
• Utility refers to quantifiable measure of performance or
worth or usefulness of specific goal in set of possible goals.
• Can also refer to a specific chain of actions amongst set of
possible chains of actions.
• Utility function maps (goal) state or a chain of states to
value which represents its performance or worth.
• U-IS design is useful when:
– several conflicting goals exist
– multiple goals are possible but only one of them is practical or
achievable.
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U-IS: Adaptive transport scheduling
scenario
• 2 conflicting goals to recover from disruptions to
designated schedule are:
– maximise revenue by maximising pickup load maintaining a quality
of service
– minimising deviations from a designated schedule.
• The greater load picked up, the later an already late vehicle
remains or gets worse.
• A utility function weighs revenue generation & maintaining
punctuality
– E.g., ??
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Overview
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•
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•
•
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•
•
•
•
Types of Intelligence and IS Model
Reflex & Environment Models for IS
Goal and Utility based models for IS
Hybrids IS Models 
KB-IS Models
KB Management: Creation & Deployment
Knowledge Representations: rules, BB, semantics
Logic reasoning Models for IS
Soft Computing IS
Generic IS Operations
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Hybrid IS (H-IS)
• H-IS models are more complex and aim to combine the
benefits of the individual IS models.
• 2 basic designs:
– Horizontal concurrent layers
– vertical (sequential) layers.
• Layers consist of single or multiple IS components with a
clearly defined interface for input and output.
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H-IS: Vertical vs. Horizontal Model
Designs
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Hybrid IS (H-IS): Horizontal Layers
• Horizontal homogeneous layered model (Section 8.3.2)
allows multiple events to be handled in parallel.
• This type of model just needs to be generalised to allow
heterogeneous models to be layered.
• H-IS models can be ‘layered’ in a single IS
• H-IS can handle reactive events in lower reactive layer
• Then handle events which require use of environment
model & reasoning in a higher layer.
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H-IS: Horizontal Layered Model
• Design challenge with this type of model is that multiple
output action events can occur for the same input event.
– because each layer independently outputs its own action.
• Problems ?
• Solutions?
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H-IS: Vertical Layered Model
• Simplest chaining is a type of single-pass vertical model
designs in which control flows through each layer in order
to generate the action in the last layer.
• Other types of vertical model design?
– could use multiple passes or flows for control, information and
action generation.
• Challenges?
– Does not allow concurrent event processing tasks to occur and can
form a processing bottleneck.
– If any component fails the whole chain could fail without design
support to prevent this.
• Solutions?
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Embedded Control System & Robots
based upon R-IS , U-IS
• Simple control systems could be based upon R-IS & U-IS
• Discuss …
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Hybrid L-IS, G-IS Vs. Autonomous
Systems
• Autonomous systems are similar to IS in the sense that
these can be goal-directed and policy constrained.
• Discuss …
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H-IS Applications Can Operate in
Multiple Environments
• UbiCom systems are sometime needed to operate in
multiple environments that are chained
• Process events in model-based human environment, then
in goal-based model, then in human reactive model and
finally in a physical world reactive model.
• e.g., in adaptive transport scheduling scenario:
– transport system needs to be aware of human environment. Why?
– Transport system needs to be aware of phys. Env. Why?
– Transport system may be driven by goals & utility functions Why?.
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Hybrid IS: Vertical Layered Model
• Example of use of hybrid IS Vertical Layered Model for
UbiCom that models reactive human events, reactive
physical events, model of human environment and models
of goals.
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Hybrid IS: Horizontal Layered Model
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Hybrid R-IS, EM-IS and U-IS Application:
Adaptive Transport Scheduling scenario
• IS represents transport, e.g., bus, service
• Use utility functions to weight the importance of different
independent factors that affect a goal,
– e.g., ….
• Prediction of a bus’s arrival at scheduled bus-stops is
based upon a model of how a bus’s environment,
– e.g., ….
• IS to support this scenario could be based upon a hybrid RIS, EM-IS and U-IS design.
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Hybrid R-IS & G-IS Application:
Foodstuff Management Scenario
• Discuss ...
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Hybrid R-IS & G-IS Application: Utility
Regulation Scenario
• Discuss …
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Overview
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•
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•
•
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•
•
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Types of Intelligence and IS Model
Reflex & Environment Models for IS
Goal and Utility based models for IS
Hybrids IS Models
KB IS Models 
KB Management: Creation & Deployment
Knowledge Representations: rules, BB, semantics
Logic reasoning Models for IS
Soft Computing IS
Generic IS Operations
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Knowledge based (KB) IS Models
• A range of IS models in terms of:
– representation,
– operations
– what the KB model is used for (the type of KB model)
• Commonly used types of KB system architectures include:
– Blackboard systems & EDA systems
– Production systems or rule systems
– Semantic type KBs such as ontology-based systems (Section 8.4)
• Alternative Terms are sometimes used somewhat
synonymously by some is this correct?
– knowledge-based,
– semantic
– ontology-based models
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KB Models: Benefits
•
•
•
•
•
to share a common understanding
to enable the reuse of domain knowledge,
to make domain assumptions explicit,
to separate domain from operational knowledge
to analyse domain knowledge
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KB Characteristics
• KB behaves as a surrogate
• KB is a set of ontological commitments
• KB is often used for intelligent reasoning
• KR acts as a machine readable & understandable
language
• KR acts as a human readable &understandable language
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What is Knowledge?
•
•
•
•
Know-what?
Know-how?
Know-why?
Experiences?
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KB IS Management Life-cycle
• KB Manual Creation vs. Automatic Acquisition
• KB Deployment & Maintenance
– Validating
– Updating in a consistent way
• KB Management & services: store, retrieve, share KB
models
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Why ISs Use Knowledge Models
1. EM-IS: Knowledge and reasoning play a crucial role in
dealing partially-observed environments
2. EM-IS: Knowledge and reasoning play a crucial role in
dealing with sequential environments:
3. Multi-ISs: enhances interoperability
4. Multi-ISs: enriches information and task sharing
5. Multi-ISs: enable reuse of domain knowledge
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Overview
•
•
•
•
•
•
•
•
•
•
Types of Intelligence and IS Model
Reflex & Environment Models for IS
Goal and Utility based models for IS
Hybrids IS Models
KB IS Models
KB Management: Creation & Deployment
Knowledge Representations: rules, BB, semantics
Logic reasoning Models for IS
Soft Computing IS
Generic IS Operations
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What is Modelled in each IS?
•
•
•
•
R-IS: EDA rules
EM-IS: EM
G-IS: goals and plans
U-IS: weightings or goals or tasks
• Seems a useful model for representing personal preferences
– When and how are these model defined?
– When how are they populated with information?
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How the models in an IS are acquired
• Defined at fixed design time by humans. Data to populate
the model can be acquired at run-time:
– From humans
– From other ISs, knowledge-sharing (Chapter 9)
– From ‘non-intelligent ‘data repositories, e.g., databases (Chapter 3)
• Defined at design time by humans so that models can
improve themselves
– i.e., incorporate learning
– Any of the models in any type of IS can be learn
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Knowledge Life-cycle & Management:
Creating Knowledge
• In order to create a useful & accurate application domainbased knowledge based system need combination of:
– an understanding of the problem
– a collection of heuristic problem-solving rules from experience
• Earliest type of KB system tended to use the knowledge of
one or more human domain experts for source of a
system's problem solving strategies
– Often referred to as an expert system.
– E.g., ???
– etc
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Knowledge Life-cycle & Management:
Creating Knowledge
• KB consists of concepts and their properties , concept
relationships and property relationships
• A concept represents an idea of something that could be a
real world object or an abstract object such as human
behaviour, or feelings etc.
• Creating an Ontology for devices, e.g., all device concepts
that have:
– either a microprocessor, microcontroller or central processor unit,
– have memory, have an input interface, have an output interface,
have a network interface, are collectively described as ‘devices’.
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Example Domain Ontology for
Devices
Link labelled graph: uses multiple types of link, e.g., W3C RDF, RDF-S, OWL etc
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Design Issues: Creating a Device
Ontology
• In general, all such concepts do not have any absolute
definition;
• E.g., , concept of device is defined in terms of its
relationship to pre-existing concepts
• Hence, concepts are understood through their relationship
to other concepts, which have already been understood
and remembered.
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Creating a Domain Ontology: Manual
vs. Automatic Process
• Process is manual?
– e.g., HCI UCD like
• Process is automatic?
– E.g., uses machine learning, data mining
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Creating a Domain Ontology: Process
• The process of creating an Ontology for a domain consists
of defining:
–
–
–
–
Concept taxonomy
A set of relations
Constraints
Axioms
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Creating a Domain Ontology Process:
Design Issues
• There are many different variations of the creation process.
• The ease and explicitness at which complex relationships
can be modelled depends upon the type of KR: light-weight
versus heavy-weight.
There are many modelling choices to be made in Ontology
design as in any kind of design:
• Categorisation choices
• Model relationships which have different cardinality
• Modelling the direction & symmetry of relationships
• Modelling choices about constraints
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Ontology Refinement & Validation
• Often a knowledge model which is created requires a
process or refinement in order to improve it
• Ontology Model needs to be validated. Why?
• How?
• Once an Ontology is defined & agreed, can it vary?
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Knowledge Life-cycle & Management:
Maintaining Knowledge
Design issues
• Can the knowledge model then remains fixed during
deployment?
• Or do knowledge models in practice need to change
• Or multiple knowledge models may exist ?
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Knowledge Life-cycle & Management:
Maintaining Knowledge
In practice, knowledge models may vary within a domain
Why?
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Maintaining Knowledge: Handling
Heterogeneous Models
• How to deal with multiple heterogeneous knowledge
models within a domain?
• Interoperation between heterogeneous ontology models
– Merging or integration
– Alignment
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Learning-based IS (L-IS)
• Learning refers to system improving its performance with
experience, with respect to some task.
• System is said to learn from experience E with respect to
some class of actions A and performance measure P, if its
performance at the set of actions A, as measured by P,
improves with experience E.
• Adaptive transport scheduling example:
– A = “a logistics vehicle picks up goods on route”,
– E = “travelling the route”, P = “deviation of actual time from
predicted time”.
– Improvement is measure P reducing to zero.
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L-IS
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L-IS
• Design of the learning element depends on:
– what (which model) is learned,
– the type of feedback and the model or knowledge representation.
• Learning may need model representations that can handle
uncertainty
• 3 main types of learning or feedback which can be used:
– supervised learning,
– unsupervised learning
– reinforcement learning.
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Overview
•
•
•
•
•
•
•
Types of Intelligence and IS Model
Reflex & Environment Models for IS
Goal and Utility based models for IS
Hybrids IS Models
KB IS Models
KB Management: Creation & Deployment
Knowledge Representations: rules, BB,
semantics 
• Logic reasoning Models for IS
• Soft Computing IS
• Generic IS
Operations
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Knowledge Representations (KR)
Commonly used types of Knowledge Representations:
• Blackboard systems & EDA systems
• Production systems & rule-based systems
• Syntactical: RDBMS, XML Web services
• Semantic type KBs such as ontology-based systems
• Classic-logic based
• Soft Computing
Which type of knowledge can these express well, express
less well?
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KB Systems: Production or RuleBased
• In a production system, knowledge is represented as a set
of rules or productions stored in a KB
• Rules are typically defined as if IF-fact THEN-fact
– IF-fact (also called the condition part or antecedent part)
– THEN-fact (also called the consequent part or action part)
• Rule-based KB model can be combined with a reactive
type IS.
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KB Systems: Rule-Based
Adaptive Transport Scheduling Service Scenario example
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KB Systems: Rule-Based
• Give some examples of rule-based engines fragments for
the other 3 scenarios:
– Personal memories
– Foodstuff management
– Utility Regulation
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KB Systems
• Rules can get added to the KB:
– Manually, e.g., enterprise policy-based systems
– Automatically, e.g., machine learning.
• Many rule-based engines have been developed
– They enable rule-based systems to incorporated as part of more
general distributed systems versus as part of more specialised IS,
– e.g., JESS
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Rule-based KB Systems: Challenges
• ???
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Rule-based KB Systems
• Rule-based KB vs. Semantic KB?
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KB System: Blackboard (BB)
• BB systems act as a shared knowledge type data
repository
– For use between multiple possibly distributed processes
– See Section 3.3.3.7
• Knowledge sources can be:
– Independent & distributed
– heterogeneous
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KB System: BB vs. EB
KB vs. Event-based system (EB)?
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Semantic IS: KRs
• Ontology based models support a rich semantic
conceptualisation & can directly support reasoning
– Huge amount of research interest, less mainstream industry use –
but this depends on how terms ontology, semantics are defined.
• Often term Ontology, expressed informally as a collection
of descriptions of the world that helps users define the
meaning of their actions on the world, is used
synonymously with the term KR
• Ontologies have many more formal definitions
– e.g., “A formal, explicit specifications of a shared conceptualization”
– etc
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KR: Multiple Semantic Representations:
Light-Weight to Heavy-Weight
• There exists a range of ontology models and
representations depending how concepts and their
relationships are defined and organised.
• Light-weight
• Medium-weight
• Heavy-weight
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KR: Light-Weight Representations
• Have simple conceptualisation having parts such as values
of terms that may not be machine-readable and machinerelatable to other terms.
– E.g., dictionaries
• Currently, the most widely used light-weight KRs based
upon W3C Web XML
• Defines an unnamed hierarchy of concepts & properties
• Acts as a basic node labelled graph representation
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Light-Weight KR: Node-Labelled
Graph
Node labelled graph: uses one type of inter-node link and intra-node link, e.g., W3C XML
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KR: Light-Weight Representations
• XML is an extensible language designed for exchanging
extensible application specific hierarchical data structures.
• XML extensions Is used for SOC (Section 3.2.4)
• Data structures are certainly machine readable
• XML is a difficult data format on which to build automated
machine-understandable processing and to support
interoperability between autonomous heterogeneous Web
services.
• Extensions to this are heavier weight and developed as
part of the Semantic Web (SW).
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Medium-Weight KR: Edge Labelled
Graph
Link labelled graph: uses multiple types of link, e.g., W3C RDF, RDF-S, OWL etc
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KR: Heavy-Weight Representations
• These support more descriptive conceptualisation and
more expressive constraints on terms and their
interrelationships including logical constraints
• Regardless of the properties of the specific Ontology,
heavier-weight Ontologies generally include:
–
???.
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KR: Heavy-Weight Representations:
Which Types of Logic
• Heavy-weight semantic KRs that are also logical KRs
• Which type of logic should be combined with the semantic
representation to support?
–
• How to support more flexible reasoning?
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Semantic Web
• Semantic Web (SW) was created:
– to evolve the Web from machine-readable to > machineunderstandable
– to support richer service interoperability
• SW defines a suite of KRs with different weights
– RDF
– RDFS
– OWL
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Semantic KR: Design Issues
• Open World versus Closed World Semantics
• Knowledge Life-cycle and Knowledge Management
– Creating Knowledge
– Knowledge Deployment
– Maintaining Knowledge
• Design Issues for UbiCom Use
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KR Design: Open World versus
Closed World Semantics
• RDBMS type KB models tend to assume closed-world
semantics,
– if data is not present, it is false (negation as failure).
• In contrast semantic type KBs tend to use open-world
semantics
– regard absence as unknown (negation as unknown).
• Is there an Open World versus Closed World Semantics
clash?
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KR Design: Semantics is Variable or
Undefined
• Semantics for the same concept may be dynamic, depend
on context
• Understanding of semantics varies across heterogeneous
users
• Semantics of the data is often defined not to be declarative
– ??
• Application semantics are often not explicit and not
accessible from the data
• Solution
– ??
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KB IS: Creation Tools
• Several interactive knowledge creation tools available
• Enable less expert and specialised developers & users to
create knowledge models
• Then to export the knowledge model out of the tool in a
form that can be imported, interpreted or parsed and then
invoked via an API by computer applications
– E.g., Protégé
– E.g., Jena
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KR Design for UbiCom Use
• ICT resources in some devices, particularly, mobile, ASOS
and embedded devices are limited by design.
• May not be possible to handle semantic information &
commands well on the device.
• Length of time a computation takes affects the semantics?
• Designs for KR for IS operations need to be selected to ?:
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KR Design for UbiCom Use: Device
Only have Access to Partial Models
• Devices embedded into a local environment often have
partial view rather than a global view of their environment.
• Systems today often create information in a far more
decentralised manner than they did in the past. Why?
–
• Solutions?
–
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Lecture Outline
•
•
•
•
•
•
•
•
•
•
Types of Intelligence and IS Model
Reflex & Environment Models for IS
Goal and Utility based models for IS
Hybrids IS Models
KB IS Models
KB Management: Creation & Deployment
Knowledge representation: rules, BB, semantic
Classic Logic IS Models 
Soft Computing IS Models
IS System Operations
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Classical Logic IS
• Propositional and Predicate Logic
• Reasoning
• Design Issues
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Classic Logic
• Classical logic based upon first-order predicate true or false
logic as being at the heart of any IS which needs to support
reasoning about the system.
• These type of IS are also commonly referred to as (logic)
reasoning systems, deliberative IS, and as symbolic AI
because these systems involve the manipulation of
symbols in the form of logic formulae, although in general
symbols could also refer to any mathematical formulae
including algebraic formulae.
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Propositional Logic
• Propositional logic : knowledge represented in the form of
relations which are either true or false.
• Multiple propositions can be combined to form sentences
using logic operators that are either true or false
• The standard logic operators are:
– And, or, not, equals, implies
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Predicate Logic
• Predicates are defined to support more expressive
sentences than propositions
– allow a property to be related to some object or
– a property related to some value.
• Sentence "Device A is in hibernate mode", is a predicate
• Can be evaluated to a proposition,
– e.g., mode (Device A, Hibernate).
• Most common form of Predicate logic is called First-Order
Predicate Logic or FOPL.
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Description Logics (DL)
• Based Upon combining FOL with a conceptualisation
organisation based upon graphs, e.g., RDF-S
• Used extensively in Heavy-weight Ontology languages,
Semantic Web, e.g., OWL
• Benefits?
• Consist of ?
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Reasoning
• Reasoning or inferencing, involves logical operations on
logical sentences or statements within a (logical) model,
bounded by an application domain etc, in order to draw
conclusions and to derive other sentences,
– e.g., A entails B, A |= B. Inferencing is used to search for
entailments.
• Sometimes multiple possible worlds or models will be
possible. Why not?
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Reasoning: Model checking
• Model checking used to check that entailments of
sentences are valid in all possible worlds or models.
• Valid sentences are called tautologies.
• Sometimes it is just necessary to check if a sentence is
true in some specific model, i.e., it is satisfied in that model
rather than being able to say it is true in all models, i.e., it is
valid in all models.
• Model checking can involve changing logical restructuring,
changing the syntax of logical sentence whilst keeping the
semantics the same, in order to make checking the logical
equivalence of two sentences easier.
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Reasoning:
• Resolution type inferencing can result in lengthy
computation.
• This is particularly an issue in resource constrained devices
Solutions?
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Reasoning: Design Issues
For more pervasive use of logic-based IS that supports
reasoning etc.
• Reasoning needs to be scalable
• Reasoning needs to be selectively used
• Reasoning when it occurs needs to be computationally
efficient.
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Reasoning: Design Issues
Benefits of reasoning using FOL ?
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KR Systems Based Upon Classic
Logic: Challenges
•
•
•
•
•
•
Difficulty in expressing exceptions
Imprecision
Uncertainty
High computation maybe needed to establish truth
Logical inconsistencies can occur, e..g, in a distributed KB
Different sub-types and extensions to classical logic exist
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Lecture Outline
•
•
•
•
•
•
•
•
•
•
Types of Intelligence and IS Model
Reflex & Environment Models for IS
Goal and Utility based models for IS
Hybrids IS Models
KB IS Models
KB Management: Creation & Deployment
Knowledge representation: rules, BB, semantic
Classic Logic IS Models
Soft Computing IS Models 
IS System Operations
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Soft Computing
• Many decisions which involve interaction with humans and
the physical world are soft
– rather than being expressed as either true or false.
• Are more qualitative and may involve some imprecision
and uncertainty.
• Such systems can be designed using soft computing
techniques,
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Probabilistic Networks
• Probabilistic network, also called a Belief network or
Bayesian Network (BN)
• How to model the likelihood of indeterminate events
happening or to model the degree of belief in a proposition
or predicate and then to reason about them?
• Must consider a prior or unconditional probability and
conditional or posterior probabilities
• Product law expresses a conditional probability in terms of
another conditional probability and two unconditional
probabilities
• This is the basis of probabilistic inferencing.
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Bayesian Network (BN)
• Bayesian Network (BN)can be used to represent any full
joint probability distribution.
• BN can be used to inference in a context-aware UbiCom
scenario in which there are both non-deterministic
preconditions and non-deterministic outcome.
– E.g., in the adaptive vehicle scheduling scenario, both passengers
and buses can indeterminately arrive at pickup points and humans
and vehicles can indeterminately wait (Figure 8-11).
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Bayesian Network application: Adaptive
Transport Scheduling Scenario
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Fuzzy Logic
• Fuzzy logic can represent a where model
– the outcome of a proposition is deterministic
– but is somewhat approximate or imprecise
– E.g., the vehicle is travelling very slowly, or slowly, or at a moderate
speed, or fast or very fast.
• This kind of imprecision can also be used in fuzzy rules.
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Fuzzy Logic Application: Adaptive
Transport Scheduling Scenario
• Example fuzzy logic rule could be
– If the bus is travelling 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.
• Here, the terms slowly, quickly, and near, act as fuzzy
descriptors.
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Fuzzy Logic Application: Adaptive
Transport Scheduling Scenario
• Why wouldn’t crisp logic work here?
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Lecture Outline
•
•
•
•
•
•
Types of Intelligence and IS Model
Reflex & Environment Models for IS
Goal and Utility based models for IS
Hybrids IS Models
KB Management: Creation & Deployment
Knowledge Representation: rules, BB,
semantic
• Classic Logic IS Models
• Soft Computing IS Models
• IS System Operations 
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IS System Operations
• Searching
• Planning
• Reasoning but is specific to a specific logic representation
• Learning: requires architectural support for feedback
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Searching
• Is a problem-solving technique that systematically explores
a space of problem states,
– i.e., successive and alternative stages in the problem-solving
process
– in order to select a goal state or a chain or path
– through intermediate states to achieve a goal state.
•
Space of alternative solutions is searched to find an
answer
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Searching
• Much of the early research for state space search was
undertaken using common board games. Why?
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Searching: Applications
Hence search problem is expressed as:
• Start state
• Goal state
• Goal test function
• Utility function
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Searching: Algorithms
•
•
•
•
Uninformed search
No hints are available about how to reduce search space
Problem search space can be represented graphically
Searching involves traversing graph bread-first or depthfirst and testing each node to check if it is the goal-state.
• Forward-chaining: Uninformed problem space searches
tend to operate in the forward direction (progression) from
start state to end goal state
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Uninformed or Brute Search:
Breadth First Search
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Uninformed Search: Depth 1st
Search
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Uninformed Search Algorithm: Design
Challenges
• In some cases, the search algorithm must also handle the
case when the IS system cannot identify its start state
– E.g., a transport vehicle is lost and requires a route to a destination
• Mode of the search space may not be uniform,
– E.g., different nodes may have different numbers of branches.
• Also problem spaces may not be single valued but may be
multiple-valued.
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Uninformed Search Algorithm :
Design Challenges
• Some problems such as games can generate extremely
large search spaces,
– require large amounts of computation, for uninformed search
techniques
• Depth-first can fail. Why?
–
• Hence, use of variations of informed searches. Which?
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Informed search techniques
• Informed search is a general solution to reduce the
computation of an uninformed search
• Uses problem specific information to limit the problem
space.
• Core component of an informed search is a heuristic
function
– a function which depends upon the current node in a problem
space,
– e.g., a cost function rich returns a value to reach that current node
from a previous node.
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Informed search techniques
• What if nodes represent physical locations, what could cost
function represent?
–
• Heuristic function which maps each node to a value
depends on information about problem.
• Variation of cost function to assign a first cost to reach
current node from previous link to assign a second cost to
go from current node to goal node.
• Cost heuristic is used by A*search
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Informed Search: A* Search
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Searching Applications: Information
Retrieval
• A core application for searching in general is information
retrieval.
• Aim to reducing the cost in terms No. of goal tests for each
node
• How are more efficient information searching achieved?
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Classical (Deterministic) Planning
Major planning applications?
• Modern planners can be used in embodied software robots
or agents as well as for complex adaptive control in
machines such as particle beam accelerators.
• etc
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Planning
• Planning involves searching for a plan and then executing
the plan.
• Searching for a plan uses the following:
– a planning model representation;
– backward chaining to determine chains of actions which lead to the
goal state,
– forward chaining to reach the goal state from the current state;
– informed search techniques
– problem decomposition
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Planning
• The planning model represents:
–
–
–
–
–
States
Goals
Actions which transition states towards goals
Chains of actions to between non-adjacent states
Heuristic cost functions to allow the choice of multiple chains of
actions to be constrained using some heuristics
• E.g., In adaptive transport scenarios example, system must
pick-up multiple passengers in multiple locations,
Constraints:
– By path between locations to minimise fuel consumption or
– Time taken or some combination of both these.
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Planning
• Can be modelled as a graph where the nodes represent
states and the link between nodes represent actions.
• Links representing actions are not labelled to identify the
actions
–
• How to define actions?
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EDA Action vs. Planning Action
Definitions
• ????
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Planning
• How : to make complex planning problems more solvable?
• Make use of decomposition
• E.g., HTA
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Hierarchical Task Plan for watch AV
content goal
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Planning: Partial Order Planning
• Limitation of the forward and backward state searches?
– ???
– ???
• Solution?
– Partial-order-planning or POP
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Partial Order Plan Application: to
watch AV content
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Planning & G-IS Design
• For G-IS design, planning is used to enable a chain of
actions to be selected that will achieve the goal.
• Hence, any action executed is part of the plan.
• For some types of system interaction, the environment
events may trigger actions for which there is no current
plan or goal, a situated action.
• Situated action could in this case trigger a goal-based IS
design to form a plan for it.
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Non-Deterministic Planning
• However, some environments may be non deterministic
and partially observable models and here classical
planning will fail. Why?
–
• Solutions?
– Use Contingency planning or conditional planning
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Planning Application: Location
(Context) awareness
Start Context
start
Move Forward
Planned Current Context
Move To Side
Context Deviation
Re-plan & Move forward
Goal Context
Planned Current Context
Move To Side
Context Deviation
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Lecture Outline
Types of Intelligence and IS Model 
Reflex & Environment Models for IS 
Goal and Utility based models for IS 
Hybrids IS Models 
KB IS Models 
KB Management: Creation & Deployment 
Knowledge Representation: rules, BB,
semantic 
• Classic Logic IS Models 
• Soft Computing IS Models 
• IS System
Operations
 environments and interaction
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•
•
•
•
•
•
•
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Summary & Revision
For each chapter
• See book web-site for chapter summaries, references,
resources etc.
• Identify new terms & concepts
• Apply new terms and concepts: define, use in old and
new situations & problems
• Debate problems, challenges and solutions
• See Chapter exercises on web-site
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Exercises: Define New Concepts
• AI
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Exercise: Applying New Concepts
• What should UbiCom support AI?
• etc
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