TOWARDS A CHANGE-BASED CHANCE DISCOVERY

Zhiwen Wu and Ahmed Y. Tawfik

School of Computer Science, University of Windsor,401 Sunset Ave.,Windsor,Ontario N9B 3P4, Canada

Keywords: Chance discovery, knowledge base, relevance, planning, ontology.

Abstract: This paper argues that chances (risks or opportunities)

can be discovered from our daily observations and

background knowledge. A person can easily identify chances in a news article. In doing so, the person

combines the new information in the article with some background knowledge. Hence, we develop a

deductive system to discover relative chances of particular chance seekers. This paper proposes a chance

discovery system that uses a general purpose knowledge base and specialised reasoning algorithms.

1 INTRODUCTION

According to Ohsawa and McBurney (2003), a

chance is a piece of information about an event or a

situation with significant impact on decision-making

of humans, agents, and robots. A ‘chance’ is also a

suitable time or occasion to do something. A chance

may be either positive –an opportunity or negative –

a risk. For example, predicting a looming earthquake

represents a “chance discovery”.

Many approaches have been applied to chance

discovery.

Rare events may represent chances

known to co-occur with important events, while the

important events can be extracted using data mining

techniques. KeyGraph, the application of this

technique, was applied to various data, such as

earthquake sequences, web pages, documents

(Ohsawa et al., 1998; Ohsawa and Yachida, 1999;

Ohsawa, 2003a; Ohsawa, 2003b). Tawfik (2004)

proposes that chance discovery represents a dilemma

for inductive reasoning. Induction assumes that

current trends will carry into the future thus favoring

temporal uniformity over change. However, current

observations may lead to different possible futures in

a branching time model. Finding a proper

knowledge representation to represent all these

possible futures is important. Otherwise some

chances will be missed. Bayesian and game theoretic

approaches are presented as viable chance discovery

techniques. Abe (2003a, 2003b) considers chances

as unknown hypotheses. Therefore, a combination of

abductive and analogical reasoning can be applied to

generate such knowledge and chances can be

discovered as an extension of hypothetical

reasoning. McBurney and Parson (2003) present an

argumentation-based framework for chance

discovery in domains that have multi agents. Each

agent has a partial view of the problem and may

have insufficient knowledge to prove particular

hypotheses individually. By defining locutions and

rules for dialogues, new information and chances

can be discovered in the course of a conversation.

In this paper, we incorporate some new elements

to the chance discovery process. These elements

have implications to both the conception and

discovery of chances and can be summarized as

follows:

• Chances

are not necessarily unknown

hypotheses. Many chances result from known

events and rules. For example, applying for the

right job at the right time represents a chance

for an employment seeker as well as the

employer. In this case, the goal is clear.

However, chance discovery means that the

employment seeker applies at the proper time

and for the employer, it means to correctly

project which applicant will be better for the

job.

• In

herently, chance discovery has a temporal

reasoning component. New risks and

opportunities are typically associated with

change. An invention, a new legislation, or a

change in weather patterns may result in many

chances. Incorporating chance discovery in a

belief update process is fundamental to this

work. Chances are relative; someone’s trash

may be another’s treasure. For example, finding

a cure for a fatal disease represents more of a

chance to an individual suffering from this

condition or at risk to contact it.

111

Wu Z. and Y. Tawﬁk A. (2005).

TOWARDS A CHANGE-BASED CHANCE DISCOVERY.

In Proceedings of the Seventh International Conference on Enterprise Information Systems, pages 111-118

DOI: 10.5220/0002539601110118

 SciTePress

• To discover chances and take advantage of

them, a system which can perform deductive

reasoning is needed.

Therefore, we consider chance discovery as a

process that tries to identify possibly important

consequences of change with respect to a particular

person or organization at a particular time. For this

to happen, a logical reasoning system that

continuously updates its knowledge base, including

its private model of chance seekers (CS) is

necessary. A chance discovery process may act as an

advisor who asks relevant “what if” question in

response to a change and present significant

consequences much like seasoned parents advise

their children. Such advice incorporates knowledge

about the chance seekers, their capabilities, and

preferences along with knowledge about the world

and how it changes.

In a word, to discover chances, we need three

things: First, a knowledgeable KB which can infer

and understand commonsense knowledge and that

can incorporate a model of the chance seeker.

Second, we need a source for information about

change in the world. Third, we need a temporal

projection system that would combine information

about change with the background knowledge and

that would assess the magnitude of the change with

respect to the knowledge seeker. Cyc knowledge

base is supposed to become the world's largest and

most complete general knowledge base and

commonsense reasoning engine and therefore

represents a good candidate as a source for

background knowledge. Information about changes

occurring in the world is usually documented in

natural languages. For example, a newspaper can

serve as a source for information about change. We

need Nature Language Processing (NLP) tool to

understand this newspaper. We assume that Cyc

natural language module will be able to generate a

working logic representation of new information in

the newspaper. However, for the purpose of the

present work, understanding news and converting it

to Cyc representation has been done manually. This

paper proposes an approach for assessing the

implications of change to the chance seeker and

bringing to the attention of the chance seeker

significant risks or opportunities.

The paper is organized as follows: Section 2

establishes the notion that chance and change are

tied together. Section 3 introduces Cyc knowledge

base and its technology. Section 4 presents the

chance discovery system based on Cyc.

2 CHANCES IN CHANGES

Chances and changes exist everywhere in our daily

life. In general, changes are partially observable by a

small subset of agents. Therefore, it is more likely to

learn about changes happening in the world through

others. For example, information about change could

be deduced from conversations in chat rooms,

newspapers, e-mail, news on the WWW, TV

programs, new books and magazines, etc. In another

word, change causing events occur daily around the

world. The amount and rate of those events is very

large. However, a relatively small portion of these

changes represent risks or opportunities to any

particular chance seeker.

Initially, the system starts with a stable

knowledge base KB. The knowledge base represents

the set of widely held knowledge. As part of KB’s

knowledge, each chance seeker maintains its own

private knowledge that describes its current

attributes. In addition to KB, each chance seeker

also maintains its private goals and plans about how

to achieve those goals. If chance seeker doesn’t

maintain its goals, the system will use default goals

that are widely accepted as common goals. For

example, the system assumes that all people want to

become more famous or richer, want their family

members and relatives to be rich and healthy, etc.

We assume that the chance seeker has already

exploited the chances present in the current KB and

that the current plans of chance seeker are the best

according to current KB. However, current plans

may only be able to achieve part of the goals. For

example, the goal to own a house in Mars is

unachieved by current knowledge.

A goal of chance seeker can be represented by a

set of sentences describing a future status of chance

seeker’s attributes. For example, if chance seeker set

up the goal to be a famous scientist, the system can

judge the achievement of the goal by measuring

chance seeker’s current attributes, such as education,

occupation, published papers, social class, etc. The

system maintains an attribute framework of chance

seeker in KB. The attribute framework can be able

to change if necessary. A goal can be considered as a

future projection of current framework. On the other

hand, a future set of attributes could satisfy many

goals of chance seeker. Current plans of chance

seeker project current set of attributes to the most

achievable set of attributes.

As new information B becomes available, an

update operation is triggered. The update operation

proceeds in two phases: a explanation phase and a

projection phase. The explanation phase tries to

revise current plans that may have been proven to be

inaccurate by the occurrence of B. Similarly, the

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projection phase, revises current plans to take into

account the occurrence of B. A risk is detected if the

occurrence of B results in a threat to the causal

support for one of the plans of the chance seeker. An

opportunity is detected if B satisfies one of the

followings: the occurrence of B enables another one

of the goals of the chance seeker to become

achievable, or better plans can come up after B. In

some cases, a particular piece of new information

will result in both risks and opportunities.

3 CYC KNOWLEDGE BASE FOR

CHANCE DISCOVERY

The Cyc knowledge base (KB) (OpenCyc.org, 2002)

is a formal system that represents of a vast quantity

of fundamental human knowledge: facts, rules of

thumb, and heuristics for reasoning about objects

and events of everyday life. The medium of

representation is the formal language known as

CycL. CycL is essentially an augmentation of first-

order predicate calculus (FOPC), with extensions to

handle equality, default reasoning, skolemization,

and some second-order features. For example:

(#$forAll ?PERSON1

(#$implies

(#$isa ?PERSON1 #$Person)

(#$thereExists ?PERSON2

(#$and

(#$isa ?PERSON2 #$Person)

(#$loves ?PERSON1 ?PERSON2))),

in English, means

“Everybody loves somebody.”

In Cyc, a collection means a group or class.

Collections have instances. Each instance represents

an individual. For examples,

(#$isa #$AbrahamLincoln, #$Person).

(#$isa #$BillGates, #$Person).

Abraham Lincoln and Bill Gates are individuals.

Person is a collection. A collection could be an

instance of another collection. For example,

(#$genls #$Dog, #$Mammal),

means “Collection Dog is an instance collection

of collection Mammal”.

In other word, Dog is a specialization of

Mammal. It can be said that every individual is an

instance of Thing, which is the most general

collection in Cyc KB. Some individuals could be

part of other individuals. For example, Microsoft is

an individual. Joe works for Microsoft. Joe is part of

Microsoft.

Constants are the "vocabulary words" of the Cyc

KB, standing for something or concept in the world

that many people could know about. For example,

#$isa, #$Person and #$BillGates are constants.

The assertion is the fundamental unit of

knowledge in the Cyc KB. Every assertion consists

of:

• an expression in CycL language that makes

some declarative statement about the world

• a truth value which indicates the assertion’s

degree of truth. There are five possible truth

values, including monotonically true, default

true, unknown, default false and monotonically

false.

• A microtheory of which the assertion is part of a

theory. Section 3.1 gives a detailed explanation

of microtheories.

• A direction which determines whether

inferences involving the assertion are done at

assert time or at ask time. There are three

possible values for direction: forward

(inferences done at assert time), backward

(inferences done at ask time), and code

(assertion not used in regular inference).

• A justification which is the argument or set of

arguments supporting the assertion's having a

particular truth value.

An assertion could be a rule or a Ground Atomic

Formula (GAF). A rule is any CycL formula which

begins with #$implies. A GAF is a CycL formula of

the form, (predicate arg1 [arg2 ...argn]), where the

arguments are not variables.

In Cyc, time is part of the upper ontology. It is a

physical quantity. A temporal object such as an

event, a process, or any physical object has a

temporal extent. The time model is interval-based

with suport for points. TimeInterval has dates, years,

and so on, as its subcategories. An event is a set of

assertions that describe a dynamic situation in which

the state of the world changes. An event has non-

empty space and time components. It may also have

performer, beneficiaries, or victims. A script in

CycL is a type of complex event with temporally-

ordered sub-events. Applications can use script

recognition – that allows them to identify a larger

script from some stated events that are constituent

parts of the script. Scripts can also be used for

planning and for reading comprehension.

TOWARDS A CHANGE-BASED CHANCE DISCOVERY

113

3.1 Microtheories

A microtheory (Mt) is a bundle of assertions. The

bundle of assertions may be grouped based on

shared assumptions, common topic (geography,

football, etc), or source (CIA world fact book 1997,

USA today, etc). The assertions within a Mt must be

mutually consistent. Assertions in different Mts may

be inconsistent. For example,

MT1: Mandela is President of South

Africa

MT2: Mandela is a political prisoner

Microtheories are a good way to cope with

global inconsistence in the KB, providing a natural

way to represent things like different points of

views, or the change of scientific theories over time.

Mts are one way of indexing all the assertions in

Cyc KB.

There are two special Mts, one is #$BaseKB

(always visible to all other Mts), the other one is

#$EverythingPSC (all other Mts are visible to this

Mt). #$EverythingPSC is a microtheory which has

no logically consistent meaning but has a practical

utility just because it is able to see the assertions in

every microtheory.

The Cyc KB is the repository of Cyc's

knowledge. It consists of constants and assertions

involving those constants. It could be regarded as a

sea of assertions, see figure 1. Form ontology point

of view, the Cyc KB could also be thought of as

#$Chemistr

#$Or

anizationMt

#$Biolo

ure 1: C

c Knowled

e Base as a sea of Assertions

Figure 2: Chance Discovery Syste

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made up of layers ordered by degree of generality.

Cyc uses two rules of inference in theorem proving,

modus ponens and modus tollens.

Cyc-NL is the natural language processing

system associated with the Cyc KB. It could

translate natural language into CycL. Cyc-NL has

three main components: a lexicon, a syntactic parser

and a semantic interpreter. The lexicon along with a

generative morphology component generates part-

of-speech assignments for words in a sentence. The

syntactic parser uses a grammar to generate all valid

parses for the sentence. The semantic interpreter

produces pure CycL equivalent for the input

sentence.

4 CHANCE DISCOVERY SYSTEM

Figure 2 shows the framework of chance discovery

system. Nature Language Processing (NLP) modules

analyze daily news and generate new knowledge

which is represented in logic. The new knowledge is

then integrated into public Cyc KB servers. The

private Cyc KB server owned by the chance seeker

will connect to public KB servers and update its

knowledge. On the other hand, the chance seeker

updates its private attributes in the private Cyc KB.

The knowledge about chance seeker can be regarded

as a virtual chance seeker living in Cyc KB. A

chance seeker sets up its goals or uses default goals

in the Goals & Plans Module. New knowledge

triggers the CD modules that measure the relevance

of the new knowledge to the chance seeker. The new

knowledge is considered to be a chance candidate if

the relevance score is above a certain threshold. By

trying to revise current plans using the new

knowledge, the magnitude of this chance candidate

can be measured using a utility evaluation process.

When the magnitude of the utility is above a

specified threshold, a chance is detected. Finally, the

system visualizes the chances to chance seeker, and

revises current plans for future chance detections.

4.1 The Relevance of New Knowledge

New knowledge is relevant to the chance seeker if it

has an immediate impact on the seeker’s attributes

or on the achievability of the chance seeker’s goals.

For example, the new knowledge that shows that the

chance seeker inherited a fortune is relevant as it

changes the seeker’s wealth attribute. The new

information can affect the achievability of goals in

three ways:

• making new goals achievable,

• making some previously achievable goals

unattainable, or

• changing the cost or reward of achieving some

goals.

A goal is considered achievable if the system

finds a plan to the goal from the current state. To

impact the achievability of a plan, the new

knowledge could affect the causal support for

actions in the plan or the likelihood of success.

Testing the relevance of new information to the

chance seeker is desirable to filter out irrelevant

information. Fully testing the relevance of new

information with respect to its impact on the chance

seeker’s attributes and plans could be

computationally expensive. Therefore, we gradually

apply a series of relevance tests with increasing

computational cost. These tests are:

• testing if the new information is subsumed by

existing knowledge,

• testing for temporal relevance,

• testing for spatial relevance,

• testing for impact on the chance seeker’s

attributes, and

• testing for impact on the chance seeker’s plans.

To verify that the new information is actually

new, and is not subsumed by knowledge already in

the KB, we test if it is entailed by existing

knowledge. For example, if the KB contains

assertions indicating that Paul Martin is the leader of

the Liberal Party, that the Liberals won the largest

number of seats in the parliament and that the leader

of the party that wins the most seats becomes the

Prime Minister. It becomes redundant to add an

assertion indicating that Paul Martin became the

Prime Minister. Similarly, if KB contains a

generalization of the new information, this

information will be redundant.

The relevance of information in a dynamic

stochastic system degenerates gradually over time.

The rate of degeneration of information relevance

with respect to a rational decision maker depends on

the probabilities of change as well as on the relative

utilities (Tawfik and Khan, 2005). Cyc supports a

notion of possibility akin to probability. However, it

is unlikely that the probabilistic knowledge in the

KB will be specified fully to construct dynamic

belief networks. Therefore, we rely on the

intersection of the temporal extents associated with

temporal object in the KB to verify the mutual

relevance of temporal objects. Similarly, most

spatial effects also weaken with distance. Therefore,

it is fair to filter out new knowledge whose spatial or

temporal effects lie outside the scope of interest.

New knowledge could be divided into rules and

events (facts). We consider that the chance seeker

relies on a rule if chance seeker includes some

actions that are causally supported by the

consequences of the rule into its plan. The impact of

the rule measures the role of the rule in reaching the

TOWARDS A CHANGE-BASED CHANCE DISCOVERY

115

goals. It could be regarded as the utility changes that

are credited to the rule B. If S represents the state of

chance seeker’s attributes, then impact is given by:

impact

=V(S

)-V(S)

To assess V(S

), we consider two cases: In one

case, V(S

) may already be stated clearly in the rule.

For example, the time saving from taking a newly

built high speed train to a certain destination will be

clearly stated in the news. On the other hand, if

V(S

) is unclear, we can deduce a reasonable

hypothesis by combining the new rule and existing

rules in background KB. This hypothesis will not go

beyond the known knowledge. For example, if there

is an assertion in KB stating that all the people in the

same country speak the same language, then

communicating with all Brazilians will be the utility

of learning Portuguese for a chance seeker who

wants to travel to Brazil. Note that this utility could

be inaccurate since it is based on a hypothesis. In

general, impact

may act as a greedy measure of

progress towards the goals but does not guarantee

reaching these goals. An exogenous rule may

undermine actions in the other part of chance seeker.

When new knowledge is an event, to determine

the value of an event, we have to take other factors

into account. An event could be composed by a

bundle of assertions describing its features, such as

actions, locations, time, physical object involved,

etc. The impact of an event according a particular

chance seeker is based on the following features:

• Importance of the entities involved in the event.

To evaluate an event, we take the importance of

those objects into account. For example,

‘Microsoft’ may be considered to be a more

important company than other small companies.

However, a small company currently working

with Microsoft may be important.

• The relationship between involved objects and

chance seeker needs to be taken into account.

For example, a company owned by family

members may mean a lot to chance seeker

though it’s a small company. For example, the

chance seeker may work for this small business.

Generally, close relatives, friends, and

acquaintances are more important that strangers.

According to the above:

Where V

is a value function that takes into

account the importance/size of objects

the attributes

involved and the relationships between objects and

the chance seeker including spatio-temporal

relationships. V

tries to guess the potential change

in the chance seeker’s attributes.

A negative impact indicates that the new

knowledge is a potential threat. In the case of

irrelevant new knowledge, the impact will be inside

the range of [negative threshold, positive threshold].

The new knowledge will be integrated into KB for

future reference. On the other hand, the new

knowledge will be considered as a chance candidate

if the impact is outside the range.

4.2 The Magnitude of Chances

Here, B is the set of new knowledge that passes

the relevance tests, the system will try to revise

current plans (CP) of the chance seeker using B.

Partial Order Planning (POP) and SATplan

algorithm (Russell and Norvig, 2002) can be used to

generate new plans (NP

) by taking B into account.

In our system, SHOP (Nau et al. 1999) generates

the plans for the chance seeker. SHOP is a domain-

independent automated-planning system. It is based

on ordered task decomposition, which is a type of

Hierarchical Task Network (HTN) planning.

By adopting NP

instead of CP, the chance

seeker may be able to achieve a different set of

goals, or save less time and/or money while

achieving the same goals. All these features can be

reflected by a utility function mapping. The

magnitude of B denoted by M

is represented as the

utility difference between NP

and CP.

There could be a gap between the goals of NP

and the goals of CS. As describing in section 2, a set

of goals can be represented by a future status of

attributes important to the chance seeker. If we use a

utility function (V) to map those attributes into real

values and add them together, we can represent a

notion of preference. The change in the utilities

could be represented as:

-V

represents the difference between new plans

and current plans. If M

in the range of [negative

threshold, positive threshold], it means that NP

and

CP are roughly the same. The magnitude of B is low.

Whether B is a chance or not, there are the following

possible cases:

 Short-term setback: When B has negative effect

on chance seeker’s attribute and no threat to the

current plans, B will be ignored.

 Potential risk: When B has negative effect on

chance seeker, and threatens some of the current

plans. However, repair plans can be found such

that the new plans including the repair plans can

achieve the same goal as before. This is

considered a potential risk even though it is

possible to repair the plans because if the

)),(),(( CSObjectrelationsObjectsSizeVimpact

iEEvent

∑

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116

chance seeker proceeds with the original plans

the goals may not be reached.

 Risk: Repair plans cannot be found, NP

achieve fewer goals than before. M

is out of

range. The system will consider B is a risk.

 Short-term prosperity: When B has positive

effect on chance seeker’s attribute, and no effect

on the current plans.

 Exploitable efficiency: NP

can achieve the

same goals as CP but in significantly shorter

time or costs less. B is considered as a chance.

 Improved reliability: NP

can achieve the same

goals as before for approximately the same cost

but offer an alternative for some plan elements.

 Inefficient alternative: Exploiting B, NP

can

achieve fewer goals than before or the same

goals at a higher cost without threatning CP. B

is ignored.

 Opportunity: NP

can achieve more goals than

before. M

is significant and positive and B is

considered a chance.

 Short-term gain long-term risk: When B has

positive effect on chance seeker, threatens some

of the current plans and the plans cannot be

repaired.

 Short-term loss long-term gain: B results in an

immediate loss but enables longer term plans.

Finally, if a chance is detected, NP

will be set

as CP.

4.3 Visualizing Chances

When a chance is detected, visualizing chances is

important as the last step of chance discovery.

Sometimes chance seeker may not understand why

chances returned by chance discovery system are

chances. Visualization of chances could emphasize

on the explanation and help chance seeker to realize

chances.

A detail visualization explanation including

display of the future status of attributes of chance

seeker, display of chance seeker’s current plans, etc,

may be necessary. Kundu et al. (2002) present a 3-D

visualization technique for hierarchical task network

plans. Such visualizations will be useful for the

chance seeker to understand the interactions between

various elements in the plan.

5 DISCUSSION & EVALUATIONS

The evaluation of chance discovery (CD) systems

could be based on precision, efficiency and chance

management. As discussed in Section 1, many

previous CD approaches regard chances as unknown

hypothesises, focusing on techniques to derive

common chances, i.e. chances for all people. Our

approach focuses on knowledge management,

finding chances in known knowledge (news, WWW,

etc) for a particular chance seeker by the support of

a large and rich knowledge base. In the 2005

tsunami tragedy, scientists correctly detected the

occurrence of the tsunami, but failed to warn the

relevant people in South Asia in time to evacuate.

Hence, chances are relative.

KeyGraph, as introduced in Section 1, is a

widely used technique in CD research. Matsumura

and Ohsawa (2003) present a method to detect

emerging topic (web page as chance) by applying

KeyGraph on web pages. A “Human Genome

project” example was presented. Its benefits include

finding cures to conquer fatal illness. Two sets of

web pages (C

and C

), each containing 500 web

pages, were obtained by searching “human genome”

in Google. C

was obtained on Nov 26, 2000. C

was on Mar 11, 2001. In the output of KeyGraph,

Celera (www.celera.com), a growing HG research

website, was detected as a chance in C

because

Celera co-occurred with the most important

(foundation) websites in C

. The set of foundation

websites of C

and C

, such as NCBI (the National

Centre for Biotechnology Information), etc, is

almost the same. The following events about Celera

were reported in the meantime:

1. The Human Genome Project team and Celera

announced the completion of the draft sequence

of the human genome in June, 2000.

2. Craig Venter, President and Chief Scientific

Officer of Celera and Francis Collins, Director

of the Human Genome Project, met President

Bill Clinton and British Prime Minister Tony

Blair for the progress of the human genome

analysis.

3. Papers about the completion were published in

Nature and Science in 2001.

For a researcher in medicine whose goals include

finding a cure for genetic diseases, our CD system

would report a chance after evaluating events 1&2

and would propose new plans. The system may draw

the researcher’s attention to the draft sequence as

early as on Jun 27, 2000 because Clinton and Blair

are very important individuals. The degree of

relevance will be high. The magnitude of “the draft

sequence” will be high since it makes the

researcher’s unattainable goals achievable.

Therefore, our approach could discover chances fast.

6 CONCLUSION

This paper describes a chance discovery system

based on Cyc Knowledge base. The knowledge base

TOWARDS A CHANGE-BASED CHANCE DISCOVERY

117

works as a virtual reality. Cyc KB simulates the

development of real society by continuously

updating its knowledge. The new knowledge comes

from newspaper, magazine, and WWW, etc. The

chance discovery system searches chances in KB for

on behalf of the virtual chance seekers. By assessing

the relevance of new knowledge, the irrelevant

knowledge to a chance seeker is ignored. Then

chance in relevant knowledge is detected by

considering its impact on the current plans and the

possibility of new plans that are built based on the

new knowledge.

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