Theoretical and Practical Aspects of Knowledge Representation and Reasoning

Theoretical and Practical Aspects

of Knowledge Representation

and Reasoning

Marcello Balduccini

Drexel University

mbalduccini@drexel.edu

What is Knowledge Representation

and Reasoning (KR&R)

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• KR&R aims to describe knowledge and answer queries.– Different from commands of imperative paradigm.

• Example:– Birds fly.

– Tweety is a bird.

– Does Tweety fly?

• Today’s KR&R paradigm:– Describe knowledge.

– Describe reasoning.

– Let inference engine algorithms draw conclusions (provably correct).

• (Some) Key elements:– Commonsense.

– Non-monotonic Reasoning.

– Reasoning about Actions and Change.

About Me…

M. Balduccini, Theoretical and Practical Aspects of KR&R 2 of 22

2007-2013: Principal Research Scientist, Kodak Research Labs

Print workflow automation, production-distribution planning

2013-pres.: Assistant Research Professor, CS Dept., Drexel University

Affiliations: Inst. for Energy & Envir., Cybersecurity Institute

Area of Expertise: Knowledge Representation & Reasoning

To study reasoning as it occurs in everyday life,

To create mathematically-precise characterizations of it,

To understand how it can be automated.

Research Interests

Recent Research Projects• Reasoning-augmented information retrieval and exchange

– Bridge the gap between information provider and information consumer

– Intelligently disseminate the information to who really needs it

• Intelligent autonomous systems– UAVs, UGVs, robotics

– AI + networking, automated configuration

– Policies, constraints

• Cyber-security– Information extraction

– Malware Mitigation• Cope with constraints, trade-offs of alternative mitigation strategies

• Cyber-physical systems, smart-grids– Modeling, reasoning

• Trust elements

• Cyber-security

– Take into account• Physical components

• Cyber-physical links

• Application constraints

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Commonsense

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• A body of knowledge and reasoning capabilities that are taken for

granted by humans but are difficult to formalize precisely.

• Example:

– Birds fly.

– Tweety is a bird.

– Does Tweety fly? YES!

• A refinement:

– Birds fly.

– Penguins are birds. Penguins do not fly.

– Tweety is a penguin.

– Does Tweety fly?

NO, but how do we know?

Nixon Diamond

M. Balduccini, Theoretical and Practical Aspects of KR&R 5 of 22

• Another classical example.

• Highlights the limitations of traditional formalisms.

• Nixon is both a Quaker and Republican.

• Quakers are anti-war.

• Republicans are pro-war.

• Both war supporters and opponents are vocal about their position.

1. Is Nixon pro-war or anti-war?

Unknown

2. Is Nixon vocal about his position?

Yes

Non-Monotonic Reasoning (NMR)

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• In monotonic reasoning, the addition of new knowledge never

invalidates previous conclusions.

• In non-monotonic reasoning, previous conclusions may be

invalidated by new knowledge.

• Commonsense often exhibits a non-monotonic behavior.

• Example:

– Birds fly. Penguins are birds. Tweety is a penguin.• Conclusion: Tweety flies.

– Additional knowledge: penguins do not fly.• Conclusion: Tweety does not fly!

• NMR: one of the building blocks of the formalization of

commonsense.

Non-Monotonic Logics

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• Logic-based formalisms for capturing NMR.

• Many different flavors with their advantages and disadvantages.

• Prolog:– Easy to understand, efficient implementations.

– Its non-monotonic features are difficult to characterize precisely.

• Well-founded semantics:– Simple semantics, tractable.

– Fails to draw conclusions that humans can draw.

• Answer Set Programming (ASP):– Close correspondence of formal and informal semantics; draws

conclusions similarly to humans.

– Scalability sometimes problematic.

ASP, Tweety, and Nixon

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𝑓𝑙𝑖𝑒𝑠 𝑋 ← 𝑏𝑖𝑟𝑑 𝑋 , 𝑛𝑜𝑡 ¬𝑓𝑙𝑖𝑒𝑠(𝑋).𝑝𝑒𝑛𝑔𝑢𝑖𝑛 𝑡𝑤𝑒𝑒𝑡𝑦 .

Conclusion: Tweety flies.

𝑓𝑙𝑖𝑒𝑠 𝑋 ← 𝑏𝑖𝑟𝑑 𝑋 , 𝑛𝑜𝑡 ¬𝑓𝑙𝑖𝑒𝑠(𝑋).¬𝑓𝑙𝑖𝑒𝑠 𝑋 ← 𝑝𝑒𝑛𝑔𝑢𝑖𝑛 𝑋 .𝑝𝑒𝑛𝑔𝑢𝑖𝑛 𝑡𝑤𝑒𝑒𝑡𝑦 .

Conclusion: Tweety does not fly.

𝑎𝑛𝑡𝑖 𝑋 ← 𝑞𝑢𝑎𝑘𝑒𝑟 𝑋 , 𝑛𝑜𝑡 𝑝𝑟𝑜 𝑋 .𝑝𝑟𝑜 𝑋 ← 𝑟𝑒𝑝𝑢𝑏𝑙𝑖𝑐𝑎𝑛 𝑋 , 𝑛𝑜𝑡 𝑎𝑛𝑡𝑖 𝑋 .𝑣𝑜𝑐𝑎𝑙 𝑋 ← 𝑎𝑛𝑡𝑖 𝑋 . 𝑣𝑜𝑐𝑎𝑙 𝑋 ← 𝑝𝑟𝑜 𝑋 .𝑞𝑢𝑎𝑘𝑒𝑟 𝑛𝑖𝑥𝑜𝑛 . 𝑟𝑒𝑝𝑢𝑏𝑙𝑖𝑐𝑎𝑛 𝑛𝑖𝑥𝑜𝑛 .

𝑎𝑛𝑡𝑖 𝑛𝑖𝑥𝑜𝑛 . 𝑣𝑜𝑐𝑎𝑙 𝑛𝑖𝑥𝑜𝑛 . 𝑝𝑟𝑜 𝑛𝑖𝑥𝑜𝑛 . 𝑣𝑜𝑐𝑎𝑙 𝑛𝑖𝑥𝑜𝑛 .

Conclusion:

two sets of beliefs

Both conclude “vocal”.

“Birds fly unless there is reason to believe otherwise.”

“Penguins do not fly.”

“Quakers are normally anti-war.”

“Pro-war are vocal.”

Reasoning about Actions and

Change (RAC)

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• Goal: to reason about the effects of actions.

• Actions may have direct effects (e.g., moving a truck) and indirect effects (e.g., moving the truck’s trailer).

• A domain’s evolution can be described by a transition diagram.

• Challenge: to describe in an accurate, compact way:– What changes and what does not change.

• Key: the law of inertia (“things tend to stay as they are”)

– Cumulative description of effects:• Dynamic causal laws, state constraints, executability conditions

• One solution: use Commonsense and NMR.– The law of inertia is a commonsense statement.

– Reasoning is described as choices over multiple options.

• Domains: modeled in terms of properties, (discrete) states, and transitions.

• High-level encoding: commonsensical statements.

• Implementation: non-monotonic theories.

Putting It All Together

“Things tend to stay as they are.”

“Normally, action 𝑝𝑟𝑜𝑡𝑒𝑐𝑡(𝑓) protects

𝑓 from writing.”

“Exception: insufficient permissions.”

“Any action can occur at any step.

Sequences failing to achieve the goal

must not be considered.”

ℎ𝑜𝑙𝑑𝑠 𝐹, 𝑆 + 1 ← ℎ𝑜𝑙𝑑𝑠 𝐹, 𝑆 , 𝑛𝑜𝑡 ¬ℎ𝑜𝑙𝑑𝑠 𝐹, 𝑆 + 1 .

¬ℎ𝑜𝑙𝑑𝑠 𝑤𝑟𝑖𝑡𝑎𝑏𝑙𝑒 𝑓 , 𝑆 + 1 ← 𝑜𝑐𝑐𝑢𝑟𝑠 𝑝𝑟𝑜𝑡𝑒𝑐𝑡 𝑓 , 𝑆 ,𝑛𝑜𝑡 𝑎𝑏 𝑝𝑟𝑜𝑡𝑒𝑐𝑡 𝑓 , 𝑆 .

𝑜𝑐𝑐𝑢𝑟𝑠 𝐴, 𝑆 ← 𝑛𝑜𝑡 ¬𝑜𝑐𝑐𝑢𝑟𝑠 𝐴, 𝑆 .¬𝑜𝑐𝑐𝑢𝑟𝑠 𝐴, 𝑆 ← 𝑜𝑐𝑐𝑢𝑟𝑠 𝐴, 𝑆 .⊥← 𝑔𝑜𝑎𝑙_𝑎𝑐ℎ𝑖𝑒𝑣𝑒𝑑 𝑆 , 𝑆 = 𝑓𝑖𝑛𝑎𝑙_𝑠𝑡𝑎𝑡𝑒.

Mission-Aware, Robotics-Assisted

Networks

Mission-Aware, Robotics-Assisted

Networks (MARANets)

• Problem: ensuring connectivity over large areas using limited resources

• Goal: building self-organizing sets of UVs that ensure connectivity

• Challenges:– Full connectivity is impossible

– Static connectivity is unrealistic• Mission knowledge must be used

– Complete information is unrealistic

– Unexpected events require adaptability• World knowledge, common-sense, reasoning

M. Balduccini, KR&R for Situation-Aware Operations Support 10 of 22

Marcello Balduccini

Duc Nguyen

Bill Regli

(DARPA-funded)

The Problem: High-level Perspective

• Problem: ensuring connectivity over large areas using limited resources

• Goal: building self-organizing sets of UVs that ensure connectivity

• Challenges:– Full connectivity is impossible

– Static connectivity is unrealistic• Mission knowledge must be used

– Complete information is unrealistic

– Unexpected events require adaptability• World knowledge, common-sense, reasoning

M. Balduccini, KR&R for Situation-Aware Operations Support 11 of 22

Solution:• Multiple, powerful reasoning modules

• Multi-agent system

• Network-aware reasoning

• Mission knowledge is used

• World knowledge, commonsense

• Awareness of ramifications of effects

• Reasoning about other agents’ behavior

• Explaining unexpected events

State-of-the-art:Traditional AI approach:

• Communications taken for granted

Traditional network approach:

• Mission info is not used

In our target environments:

• Communications are neither reliable nor free

• Mission info is key to mission success

Scenario

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Given:– A set of radio-enabled UAVs 𝑢1, 𝑢2, …– A set of targets 𝑡1, 𝑡2, …– A (possible) set of radio relays 𝑟1, 𝑟2, …

• Network- and mission- aware planning so that:– Pics are taken of every target

– “Staleness” of pics is minimized

Problem: unexpected events may occur during mission– The mission plan may become invalid

• Decentralized reasoning and execution monitoring in order to:

– Detecting unexpected circumstances

– Explaining them if possible

– Re-planning in a decentralized fashion…

– …while dealing with incomplete info about the environment and communications

Architecture

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• Variant of Observe-

Think-Act loop, AAA

architecture [Baral,

Gelfond, 2000;

Balduccini, Gelfond,

2008]

• Reasoning about

actions and change for

domain model

• Reasoning components

implemented in Answer

Set Programming (ASP)

• Extensive use of ASP’s

features

– Non-monotonic nature

– Recursive definitions

Scenario’s Defining Moment 1

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UAV2 transmits to

Relays to be transmitted

to Home Base

Without UAV2, UAV1

would be disconnected

from Home Base

UAV1 takes picture of T2

and transmits to UAV2

Scenario’s Defining Moment 2

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UAV2:

• Observes Home

Base unexpectedly

unreachable

• Determines that at

least r5, r6, r7 must

be offline

• Finds a new plan

Note: UAV2 loses

track of UAV1 and

assumes that UAV1

will continue executing

the mission plan

Relays r5, r6, r7 go

offline unexpectedly,

interrupting connectivity

with Home Base

Modeling

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• Models of:

– Physical environment (e.g., move action)

– Communications (e.g., radio range)

– UV behavioral models

• KR-based reasoning components:

– Planning = action selection + constraints

– Anomaly detection = diagnosis in dynamic

domains, extended to UV behavior

MARANets: Conclusions

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• Network-aware reasoning is possible using a KR-based approach, and pays off

• Emerging sophisticated behavior, e.g. data-mule

• Robustness to sudden environmental changes

• Successfully tested on various scenarios of increasing (conceptual) complexity

• AAA agent architecture extends naturally to:– Control network-aware mobile agents

– Centralized mission planner

– Distributed anomaly detection, re-planning

– Reasoning about behavior of other agents

• Future:– Mission-aware network nodes viewed as intelligent agents

– Inter-agent communication for coordination

– Evaluate scalability

Automated Malware Mitigation

Automated Malware Mitigation

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• Mitigating malware:Eliminating or circumscribing malware on an infected system.

• The problem:– Many available actions (creating/deleting files, starting/stopping processes, reconfiguring firewall

and user access, …).

– Complex interdependencies, ramifications, side-effects• Killing a process releases its locked files, which in turn makes it possible to delete them.

• Moving a folder recursively moves all of its content.

– Lack of precisely defined notions:• What does it mean to mitigate malware?

• When can one claim that malware has been mitigated?

• What are the side-effects of a mitigation strategy?

• Our solution:– Representing computer system, malware as a dynamic system from Reasoning about Actions and

Change.

– Representation framework that precisely defines the notions and enables answering the above questions.

– Declarative model of computer system and malware.

– Automating the computations by translation to constraint-based languages.

Marcello Balduccini

Spiros Mancoridis

(CSRA/IExE-funded)

Computer System and Malware as

a Dynamic System

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• Transition diagram: collection of state transitions occurring as the effect of actions.

• Action languages enable compact representations.– Inertia, ramifications of actions, executability conditions.

State-Based Definitions of

Mitigation

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What does it mean to mitigate malware?

When can one claim that malware has been mitigated?

What are the side-effects of a mitigation strategy?

Automating Malware Mitigation

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• With our framework, mitigation is reduced

to planning in dynamic domains.

• Constraint-based theory 𝑀𝑟:

– Considers possible sequences of actions.

– Determines their consequences.

– Finds those that achieve a (strict/relaxed/…)

safe state.

Automated Mitigation: Conclusions

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• Representing computer system, malware as a dynamic system enables a precise characterization of mitigation.

• Developed theories of computer systems and malware.

• Mitigation can be automated with constraint-based languages.

• Empirical evaluation on simulated system, malware– 1,000 problem instances.

– 1-5 malware, 1-40 essential services.

– Success rate close to 90%.

– Solutions found in less than 2 seconds.

Thank you!

M. Balduccini, Theoretical and Practical Aspects of KR&R

Marcello Balduccini

Drexel University

mbalduccini@drexel.edu