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Page 1: Memory Modeling & Knowledge Representation - KITcsl.anthropomatik.kit.edu/downloads/vorlesungsinhalte/V4-Memory.pdf · Memory Modeling & Knowledge Representation ... Explicit and

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Memory Modeling & Knowledge Representation

Felix Putze

5.5.2011

Lecture „Cognitive Modeling“

SS 2011

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Structure of Lecture

• Introduction and Motivation

• Memory Modeling

• Knowledge Representation• First Order Logic

• Frames

• Semantic Networks

• Neural Networks

• Bayesian Networks

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Why do memory modeling?

• Any process that spans a period of time requires the handling of limited human memory capacity

• Memory access is not of guaranteed success and with instantaneous reaction time

• For Human-Machine-Interaction: User has limited capability of remembering and recalling Not all presented information is stored or available at all times

• Interaction systems should know what is on the user‘s mind and what is not

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Requests to a Memory Model

• There is a number of questions a memory model should be able to answer:• What items are currently active on the human‘s mind?

• Is a certain bit of information retrievable?

• What is associated with a certain input?

• How is memory organized?

• How is new information integrated?

Page 5: Memory Modeling & Knowledge Representation - KITcsl.anthropomatik.kit.edu/downloads/vorlesungsinhalte/V4-Memory.pdf · Memory Modeling & Knowledge Representation ... Explicit and

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Types of Memory• Squire (1992) distinguishes several distinct types of memory

and associates them with different parts of the brain:

• Declarative Memory: Explicit and conscious recollection of…• facts (semantic memory, e.g. “France is a country in Europe.”)

• events (episodic memory, e.g. “Last summer, I spend my holidaysin France.”)

• Procedural Memory: Implicitly learned skills (e.g. riding bicycle)

• Priming: Automated associations caused by frequent repetition

• Conditioning: Automatic stimulus-reflex pairs (e.g. Pawlow‘s dogs)

• In this lecture, we will focus on semantic memory

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Short-term and long-term Memory

• Short-term memory: Storage for a limited number of items• Small capacity

• Limited duration for storage (seconds), decay

• Longer storage duration requires rehearsal, i.e. periodic repetition

• Acoustically and visually coded (e.g. multiple phonetically similar items are hard to keep in memory)

• Long-term memory:• Nearly unlimited capacity

• Items can last for years without rehearsal

• Items are mostly retrieved and coded semantically, however there is a phonetic component (tip-of-tongue effect)

• Other types of memory: sensory memory, working memory

• The existence of distinct memory systems in the brain is controversial; experiments support both theories

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The magic number 7 (+/- 2)

• Miller (1956): Determined the capacity of short-term memory to be about 7 items• Estimated by having people recall sequences of digits or words

• Performance is very good for around five to six items

• Performance degrades rapidly for more items

• Miller’s conclusion: Memory span is not a function of encoding length in bit, but a function of the number of elements

• Later, Miller acknowledged that the “magic number” was a coincidence and heavily context-dependent

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Chunking and Mnemonics

• How can people remember longer phone number if their short-time memory is limited to 7 (or fewer) elements?• Most people do not remember the number 0123456789 as 0-1-2-3-4-5-

6-7-8-9 but as 01-23-45-67-89 (or similar)

• This division of information into smaller pieces is called chunking

• This is also a question of skill: A trained person can chunk a stream of binary digits into larger blocks, convert them to decimal numbers and remember those

• There are many other mnemonic techniques:• Make use of linguistic or phonetic similarities

• Construct images or stories to connect multiple items into one (e.g. „man“, „horse“, „fish“ A man riding on a horse hunting a fish)

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Controversy regarding memory limitations

• There are a lot of conflicting viewpoints on memory limitation:

• A general limit exists but is lower than seven (≈ 4 without possibility for chunking or mnemonic techniques)

• The acoustic encoding of items in short-term memory influences this capacity:• Of long words (which take longer to speak), only shorter sequences can

be remembered

• Memory span decreases when remembering phonetically similar words

• There are specialized parts of short term memory with separate capacity limits

• There is no limitation of short term memory at all (observed limitations are an effect of general scheduling conflicts)

• There is no special faculty for short term memory at all, only an attention limitation on generic memory

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Influence of Emotion on Memory

• Emotion-congruent information is encoded better• In a happy mode, we encode more „happy“ facts than „sad“ ones

• With high arousal, central information is encoded better• …while peripheral information is encoded worse

• Yerkes-Dodson law: Relation between arousal and performance is described as an „inverted u-curve“

• Consequence: Do not study memory as an isolated concept!

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Structure of Lecture

• Introduction and Motivation

• Memory Modeling

• Knowledge Representation• First Order Logic

• Frames

• Semantic Networks

• Neural Networks

• Bayesian Networks

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Atkinson‘s & Siffrin‘s Memory Model

• Incoming information is extracted from parts of sensory input, initially stored in STM and later transferred to LTM or displaced linear process

• Monolithic modeling (one model for each type of information)

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Components of Siffrin’s and Atkinson’s Model

• Sensory Memory:• Specialized for different sensory inputs (e.g. visual, auditive, …)

• Lasts for a very short time (milliseconds for visual, few seconds for aural information)

• Contains raw data, used to select relevant information (partial report)

• Decoupled from other components (localized, unconscious)

• Short term memory:• Keeps currently relevant information

• Duration of 15-30 seconds (unless rehearsed)

• Bottleneck between raw data from sensors and unlimited long term memory

• Long term memory:• Information which is rehearsed often enough is stored here

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Baddeley‘s Memory Model

• Model of short-term (or working) memory

• Three slave systems for different types of information

• Controlled by central executive

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Baddeley‘s Memory Model

• The phonological loop consists of two main parts:• Phonological store: contains ca. 2 seconds of audio information

• Phonological rehearsal: performs periodic rehearsal to keep information available ( „inner voice“)

• Evidence: Suppression of rehearsal impairs memory

• Visuo-Spatial sketchpad is divided in two components:• Inner cache: forms, color

• Inner scribe: spatial information, movement (planning)

• Visually presented information can also be transferred to the phonological loop by verbalization

• Separation between phonologic and visual system explains differences in dual-tasking: Combining one acoustic and one visual task is easier than combining two tasks of the same kind

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Baddeley‘s Memory Model

• Central Executive:• attention

• retrieval strategies

• episode forming

• Episodic buffer:• Added in 2000 as third slave system

• Contains concrete, multimodal “episodes”

• Introduced to explain memory which is not limited to one channel

• Also explains the ability to memorize a longer sequence of words which form a “story”

• Still less defined than the other two subsystems

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Cowan‘s memory model

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Cowan‘s memory model

• No distinction between long-term and short-term memory

• No division in modality-specific components

• Short-term memory is implicitly represented as activated items in memory

• Activation decays over time unless it is refreshed

• A subset of the activated items forms the focus of attention

• Theoretical foundation of the ACT-R memory model

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Structure of Lecture

• Introduction and Motivation

• Memory Modeling

• Knowledge Representation• First Order Logic

• Frames

• Semantic Networks

• Neural Networks

• Bayesian Networks

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First Order Logic

• First order logic is a traditional and still widely used knowledgerepresentation scheme

• Express knowledge in form of logical clauses

• Example:• „All humans are mortal.“ =

• Immediately benefits from established algorithms

• Models logical, concious deduction and inference

• Model everything in one unconstrained language, no meta-ontology, etc.

)()(: xmortalxhumanx

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Cyc: A Database of Human Knowledge

• Under development since 1984 by the company CyCorp, large collection of everyday knowledge („water is wet“)

• Currently contains ~500.000 items, ~5.000.000 facts

• A free version OpenCyc exists (subset of Cyc)

• An inference engine is able to deduce facts form the knowledge base

• Developed for language generation and language understanding

• Cyc uses higher order logic to increase its expressiveness:• A micro-theory describes the context in which a statement is valid

• For example the statement „vampires fear garlic“ is (only) true in thecontext „mythology“

• Introduces modal operator: isTrue(context, assertion)

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Cyc Example

• (isa BurningOfPapalBull SocialGathering)• The burning of the papal bull is an instance of of „SocialGathering“

• (relationInstanceExistsMin BurningOfPapalBull attendees UniversityStudents 40)

• At least 40 students attended the event

• (isa BurningOfPapalBull-Document CombustionProcess)

• (properSubEvent BurningOfPapalBull-Document BurningOfPapalBull)

• The actual burning event (as part of the social event)

• (relationInstanceExistsinputsDestroyed BurningOfPapalBull-Document (CopyOfConceptualWorkFn PapalBull-ExcommunicationCW))

• The thing destroyed is a member of the functionally defined collection „copies of the conceptual work PapalBull-ExcommunicationCW“

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FOL for Memory Modeling

• First order logic is typically used for modeling reasoning, deduction and inference

• No easy representation of “fuzzy”, associative processes

• As we see with Cyc, FOL may not be expressive enough to represent complex knowledge

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Frames

• Developed by Marvin Minsky in 1975

• A Frame consists of a name and several named attributes(„slots“) which can contain• atomic values or

• references to other frames

• nothing to represent partial knowledge

• Unification algorithm combines two frames by combiningatomic attributes and recursively unifying non-atomic attributes

• Related to the schema theory of cognitive psychology

[Class

Title: „Cognitive Modeling“

NumStudents: <empty>

Teacher: [Person

FirstName: „Tanja“

FamilyName: „Schultz“

]

]

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Parallels to Object Oriented Design

• The graphical representation of a set of frames can beregarded as an UML class/object diagram

• We identify…• Abstract concepts with (abstract and non-abstract) classes

• Entities with objects

• Relations with associations (compounds) and class attributes (atomicvalues)

• This analogy may…• help in the implementation of a knowledge representation

• allow the use of powerful tools which are readily available

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Memory Modeling in ACT-R

• The main building block of knowledge representation in ACT-R (chunk) is essentially a frame

• Semantic memory is handeled by the declarative module

• The declarative module makes no distinction between long-term and short-term memory

• Each item is associated with an activation value

• For retrieval from the declarative module, a request is storedin the input buffer of the declarative module

• This request is a partial description of a chunk and the modulereturns the chunk which matches this description and (if thereis ambiguity) which has the highest activation

• No partial matching of chunks which „almost“ fit thedescription

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Activation

• Activation of a chunk is the sum of two components (plus noise):

• Base Activation: Depends on the frequency and recency ofstimulations of a chunk:

• Spreading Activation (associative* activation):

• * chunk j is associated with chunk i if j is an attribute of a slot in i

)log(1

n

j

ji tB

age of jth activation of chunk i

else,))log((

associatednot are j and i if ,01

1 j

n

j

i fanSnS

sum over all chunks associatedwith the content of the goal buffer

number of chunksof which j is value of

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The Fan Effect

• Experiment lets participants learn the facts in the left column. When given the probes, they have to identify those whichoccured in the training set (target probes)

• It is easier to identify those sentences for which at least onecomponent (person or location) was rare in the training corpus

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Semantic Networks

• Goal of knowledge representation is the modeling of facts andtheir relationships

• Natural formalism are graphs with nodes representing factsand edges representing relationships

• Different forms of networks exist: • Are edges themselves semantically annotated?

• Are edges directed?

• Are edges weighted?

London

Paris

north-of

London

Paris

north-of

1

2

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Examples of semantic networks: Hierarchies

• Focus on „is-a“ relations

• Example from Porphyry, 300 AD:

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Examples of semantic networks: KL-ONE

• KL-ONE: Developed in 1979 by Brachman

• Knowledge representation framework for AI

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Examples of semantic networks: MultiNet

• Multi-Layer architecture, focus on language understanding

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Characteristics of Semantic Networks

• No predefined ontology and attributes

• Can introduce meta-knowledge directly into the network

• Natural tool for the representation of associations

• Allow the application of well-studied graph algorithms for analysis of network (connected components, distances, topology, …)

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The (extended) LTMc Model

• LTMc memory model designed as a replacement for the ACT-R declarative module

• LTM = Long Term Memory; largely follows the model of Cowan no explicit distinction between LTM and STM

• Models memory as semantic network• Each node has an activation value, similar to the activation in the

original ACT-R model

• Base activation and noise activation are similar, spreading activation is adapted to the network structure

• Items which are activated above a threshold are considered to be active

• Attention focus: Identify connected component with highest overall activation

• JAM: Stand-alone extension of to better model the dynamics of memory (e.g. topic drifts) developed at the CSL

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Spreading in the extended LTMc Model

• The graph structure of the model is well suited to model the process of spreading activation (i.e. association)

• When an item is stimulated, its activation is distributed to its neighbors in the network

• If total spreading activation received exceeds a threshold, it is further propagated in a Breadth-First-Search style

• Total activation spread by one node is constant, equally divided among all outgoing edges Fan effect

• JAM: Extension to handle topic drifts• Decay function to decrease spreading activation over time

• capping of spreading activation to control the total activation in the network

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Spreading Example

Stimulation of Author, Writer, FemaleHuman

Stimulation of ThomasHobbes, JohnLocke, DavidHume, BertrandRussel

(Excerpt from Cyc database converted to network structure; using the JAM algorithm)

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Stimulation of JaneAusten

Spreading Example

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Evaluation: „Moses Illusion“

• „How many animals of each type did Moses bring to the arc?“

• Typical answer: „two“. Correct answer: „none“ (it was Noah!)

• Can a memory model reproduce this behavior?

• Note that this is not a case of false knowledge but of partial matching

• Vanilla ACT-R does not reflect this phenomenon, but (extended) LTMC does:

Moses

Noah Arc

bring

two

BiblicalPerson

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ConceptNet

• Created at MIT Media Lab

• Huge common sense database represented as semantic net

• Not developed by experts but using a crowd sourcing approach• Data is entered by users of a webpage

• People play a “Game with a Purpose” (e.g. association games)

• Data can later be validated and weighted by other judges

• Contains subjective associations

• Easily accessible using Python interfaces

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Verbosity: A Game with a Purpose

Describer’s view

Guesser’s view

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ConceptNet: Example

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Knowledge Database WordNet

• Lexical database of English Language in form of a semantic network

• Developed since 1985 by George A. Miller in Princeton

• Main unit forming the nodes of the network: Synsets (group of synonymes with short description)

• Models semantic relations (mostly language-oriented) between synsets

• Contains more than 110,000 synsets

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WordNet: Example graph of hypernyms

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Information in WordNet

• Some relations are allowed at word level (e.g. antonym = of opposite meaning), but the majority is defined on synset level

• Examples for relations in WordNet:• Holonyms (part-of), e.g. „family“ is a holonym of „mother“

• Hypernyms (kind-of), e.g. „animal“ is hypernym of „dog“

• WordNet also contains short definitions in plain text for each term

• Also contains additional linguistic information, e.g. syntactic constraints on the use of certain words

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Soundness of the WordNet Graph

• WordNet ontology is represented such that more abstract generalizations of a word a further up in the ontology

• This is in accordance with the spreading model of LTMC

applied to the WordNet graph

• Introduction of the concept evocation, measures how much one concept brings to mind another

• Evocation creates a much denser network with weighted edges

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Neural Knowledge Representation

• Encode information in the structure of a neural network• Train network by presenting input patterns by stimulating neurons

• When stimulating learned (or similar) input patterns, the network should recognize them

• Example: Self-organizing maps (SOM)• Maps multi-dimensional input space to two-dimensional representation

• Map consists of interconnected nodes (neurons) in a plane (think of the cortex of the brain)

• Weights of each neuron are prototypes which describe to which input patterns the neuron is similar

• Idea: Different input patterns activate different parts of the neural network• Analogy: Human brain also shows different activation patterns for

different stimuli (e.g. regions for processing of visual vs. aural stimuli)

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Self-organizing map: Learning

• Initialize weights of all neurons randomly

• Iteratively adjust weights:• Present input pattern P as vector in a high dimensional space

• Compare P with all neuron weights, find the most similar one: S

• Determine the neighborhood N(S) using the network structure

• Shift the weights of N(S) to be more similar to P

• Optional: Shift all other weights to be less similar to P

• Over time, decrease the learning rate (strength of adaptation) and the neighborhood size

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Self-organizing map: Application

• Example: We train a SOM with pictures of different objects• Input patterns are the raw pixels or low-level descriptors

• For each category of objects, a typical activation pattern will emerge in the network• For each category, we expect a region of matching nodes

• Network learns a generalizing mapping from input patterns to categories

• SOM now can map unseen input patterns to a category

• Note that there are many other connectionist approaches (e.g. Hopfield Networks)!

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Self-organizing map: Example

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Belief

• Up to now, knowledge was either (subjectively) false or true, i.e. part of the individual knowledge base or not

• Fuzziness was part of the model concerning the activation value, not the truth of an information

• Not a realistic assumption

• Introduce belief: Degree to which some information is considered to be valid

• Example: I estimate the probability that P!=NP to be 5% (I am “pretty sure”, but there is room for doubt)

• Belief is subjective, depends on prior assumptions and experience or observations

• Need to find a formalism to model and manipulate belief

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Probability according to Bayes

• Representation of belief as probability

• Probability according to Bayes: „Confidence in the personal assessment of an issue.“

• Can be different for different individuals with different background and experience

• Model probability of non-stochastic and unique events

• Example: P(student A passes the exam on cognitive modeling)

• This is not possible in classic frequentist statistic which isdefined based on the frequency of events

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Bayes‘ Theorem

• Important Instrument: Bayes‘ Theorem

• Bayes‘ Theorem allow the combination of a-priori knowledgeP(A) with information from a cue B to calculate the a-posteriori probability P(A|B)

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Application of Bayes‘ Theorem

• Important Instrument: Bayes‘ Theorem

• Example: A = Person X is rich (true/false)B = Person X wears expensive jewelry (true/false)

• P(A=true|B=true)?

• P(A=true) = 0.1

• P(B=true|A=true) = 0.8

• P(B=true) = 0.2

• P(A=true|B=true) = 0.4

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Bayesian Networks

• Want to model joint distribution of multiple variables

• A Bayesian Network is a directed acyclic graph:• Nodes = Random Variables

• Arcs = Direct Causality

• Each node contains conditional probability distribution dependent on the parents in the graph

• Using Bayes‘ theorem, we can infer probabilities of some nodes given information on some of the others

family-out bowel-problem

lights-ondog-out

hear-bark

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The Bayesian Brain

• Bayesian coding hypothesis: Brain represents information probabilistically• Coding and computing with probability density functions

• Not limited to memory, targets noisy perception, planning and action execution

• Instead of deterministically modeling a concept X, model its probability density function p(X)

• Natural and expressive representation of uncertainty

• May present a generic framework for modeling cognition

• Allows seamless integration of models with statistical machine learning techniques