Theoretical and Practical Aspects
of Knowledge Representation
and Reasoning
Marcello Balduccini
Drexel University
mbalducc...
What is Knowledge Representation
and Reasoning (KR&R)
M. Balduccini, Theoretical and Practical Aspects of KR&R 1 of 22
• K...
About Me…
M. Balduccini, Theoretical and Practical Aspects of KR&R 2 of 22
2007-2013: Principal Research Scientist, Kodak ...
Recent Research Projects
• Reasoning-augmented information retrieval and exchange
– Bridge the gap between information pro...
Commonsense
M. Balduccini, Theoretical and Practical Aspects of KR&R 4 of 22
• A body of knowledge and reasoning capabilit...
Nixon Diamond
M. Balduccini, Theoretical and Practical Aspects of KR&R 5 of 22
• Another classical example.
• Highlights t...
Non-Monotonic Reasoning (NMR)
M. Balduccini, Theoretical and Practical Aspects of KR&R 6 of 22
• In monotonic reasoning, t...
Non-Monotonic Logics
M. Balduccini, Theoretical and Practical Aspects of KR&R 7 of 22
• Logic-based formalisms for capturi...
ASP, Tweety, and Nixon
M. Balduccini, Theoretical and Practical Aspects of KR&R 8 of 22
𝑓𝑙𝑖𝑒𝑠 𝑋 ← 𝑏𝑖𝑟𝑑 𝑋 , 𝑛𝑜𝑡 ¬𝑓𝑙𝑖𝑒𝑠(𝑋).
...
Reasoning about Actions and
Change (RAC)
M. Balduccini, Theoretical and Practical Aspects of KR&R 9 of 22
• Goal: to reaso...
• Domains: modeled in
terms of properties,
(discrete) states, and
transitions.
• High-level encoding:
commonsensical
state...
Mission-Aware, Robotics-Assisted
Networks
Mission-Aware, Robotics-Assisted
Networks (MARANets)
• Problem: ensuring connectivity over large areas using
limited resou...
The Problem: High-level Perspective
• Problem: ensuring connectivity over large areas using
limited resources
• Goal: buil...
Scenario
M. Balduccini, Theoretical and Practical Aspects of KR&R 12 of 22
Given:
– A set of radio-enabled UAVs 𝑢1, 𝑢2, …
...
Architecture
M. Balduccini, Theoretical and Practical Aspects of KR&R 13 of 22
• Variant of Observe-
Think-Act loop, AAA
a...
Scenario’s Defining Moment 1
M. Balduccini, Theoretical and Practical Aspects of KR&R 14 of 22
UAV2 transmits to
Relays to...
Scenario’s Defining Moment 2
M. Balduccini, Theoretical and Practical Aspects of KR&R 15 of 22
UAV2:
• Observes Home
Base ...
Modeling
M. Balduccini, Theoretical and Practical Aspects of KR&R 16 of 22
• Models of:
– Physical environment (e.g., move...
MARANets: Conclusions
M. Balduccini, Theoretical and Practical Aspects of KR&R 17 of 22
• Network-aware reasoning is possi...
Automated Malware Mitigation
Automated Malware Mitigation
M. Balduccini, Theoretical and Practical Aspects of KR&R 18 of 22
• Mitigating malware:
Elimi...
Computer System and Malware as
a Dynamic System
M. Balduccini, Theoretical and Practical Aspects of KR&R 19 of 22
• Transi...
State-Based Definitions of
Mitigation
M. Balduccini, Theoretical and Practical Aspects of KR&R 20 of 22
What does it mean ...
Automating Malware Mitigation
M. Balduccini, Theoretical and Practical Aspects of KR&R 21 of 22
• With our framework, miti...
Automated Mitigation: Conclusions
M. Balduccini, Theoretical and Practical Aspects of KR&R 22 of 22
• Representing compute...
Thank you!
M. Balduccini, Theoretical and Practical Aspects of KR&R
Marcello Balduccini
Drexel University
mbalduccini@drex...
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Theoretical and Practical Aspects of Knowledge Representation and Reasoning

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Cognitive Systems Institute Group Speaker Series presentation by Marcello Balduccini from Drexel University on August 20, 2015.

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Theoretical and Practical Aspects of Knowledge Representation and Reasoning

  1. 1. Theoretical and Practical Aspects of Knowledge Representation and Reasoning Marcello Balduccini Drexel University mbalduccini@drexel.edu
  2. 2. What is Knowledge Representation and Reasoning (KR&R) M. Balduccini, Theoretical and Practical Aspects of KR&R 1 of 22 • 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.
  3. 3. 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
  4. 4. 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 M. Balduccini, Theoretical and Practical Aspects of KR&R 3 of 22
  5. 5. Commonsense M. Balduccini, Theoretical and Practical Aspects of KR&R 4 of 22 • 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?
  6. 6. 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
  7. 7. Non-Monotonic Reasoning (NMR) M. Balduccini, Theoretical and Practical Aspects of KR&R 6 of 22 • 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.
  8. 8. Non-Monotonic Logics M. Balduccini, Theoretical and Practical Aspects of KR&R 7 of 22 • 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.
  9. 9. ASP, Tweety, and Nixon M. Balduccini, Theoretical and Practical Aspects of KR&R 8 of 22 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.”
  10. 10. Reasoning about Actions and Change (RAC) M. Balduccini, Theoretical and Practical Aspects of KR&R 9 of 22 • 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.
  11. 11. • 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.
  12. 12. Mission-Aware, Robotics-Assisted Networks
  13. 13. 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)
  14. 14. 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
  15. 15. Scenario M. Balduccini, Theoretical and Practical Aspects of KR&R 12 of 22 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
  16. 16. Architecture M. Balduccini, Theoretical and Practical Aspects of KR&R 13 of 22 • 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
  17. 17. Scenario’s Defining Moment 1 M. Balduccini, Theoretical and Practical Aspects of KR&R 14 of 22 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
  18. 18. Scenario’s Defining Moment 2 M. Balduccini, Theoretical and Practical Aspects of KR&R 15 of 22 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
  19. 19. Modeling M. Balduccini, Theoretical and Practical Aspects of KR&R 16 of 22 • 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
  20. 20. MARANets: Conclusions M. Balduccini, Theoretical and Practical Aspects of KR&R 17 of 22 • 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
  21. 21. Automated Malware Mitigation
  22. 22. Automated Malware Mitigation M. Balduccini, Theoretical and Practical Aspects of KR&R 18 of 22 • 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)
  23. 23. Computer System and Malware as a Dynamic System M. Balduccini, Theoretical and Practical Aspects of KR&R 19 of 22 • Transition diagram: collection of state transitions occurring as the effect of actions. • Action languages enable compact representations. – Inertia, ramifications of actions, executability conditions.
  24. 24. State-Based Definitions of Mitigation M. Balduccini, Theoretical and Practical Aspects of KR&R 20 of 22 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?
  25. 25. Automating Malware Mitigation M. Balduccini, Theoretical and Practical Aspects of KR&R 21 of 22 • 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.
  26. 26. Automated Mitigation: Conclusions M. Balduccini, Theoretical and Practical Aspects of KR&R 22 of 22 • 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.
  27. 27. Thank you! M. Balduccini, Theoretical and Practical Aspects of KR&R Marcello Balduccini Drexel University mbalduccini@drexel.edu

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