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A Default-Logic Framework for Legal Reasoning in Multiagent Systems

A Default-Logic Framework for Legal Reasoning in Multiagent Systems. Vern R. Walker Hofstra University School of Law AAAI Fall Symposium, 2006 Interaction and Emergent Phenomena in Societies of Agents. The logical structure of law emerges from:.

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A Default-Logic Framework for Legal Reasoning in Multiagent Systems

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  1. A Default-Logic Framework for Legal Reasoningin Multiagent Systems Vern R. Walker Hofstra University School of Law AAAI Fall Symposium, 2006 Interaction and Emergent Phenomena in Societies of Agents

  2. The logical structure of lawemerges from: The great multiplicity of jurisdictions, courts, and cases, and over time The rule of law: • Similar cases to be decided similarly • Each case to be decided on its particular evidence • Each case to be decided through reasoned decision-making • Maximize transparency of reasoning

  3. Factfinding in law always balances: • The epistemic objective: to increase the accuracyof findings of fact that are warranted by the available evidence Against • Non-epistemic objectives, e.g. – • Protecting public health • Deterring criminal activity • Increasing governmental efficiency

  4. Law has evolved a logicwith three major components: • Rule-Based Reasoning (creates uniformity, predictability, and transparency) • Evidence Evaluation (creates flexibility in applying the rules) • Process-constrained Decision-Making (creates uniformity, constrains discretion, promotes evolution)

  5. The “Default-Logic Framework”: Automation (e.g., Legal ApprenticeTM Software) Default-Logic Frameworkrepresents the rules, assertions, reasoning, … Legal texts(statutes, regulations, decisions, reports, transcripts, …) containing natural, legal, scientific, or other specialized language.

  6. I. Rule-Based Reasoning Modeled in Default-Logic Framework: • Conditional propositions (“if …, then …”) • Three-valued logic – True / Undecided / False • Three truth-functional connectives – Conjunction (“AND”) Disjunction (“OR”) Defeater (“UNLESS”)

  7. Implication Trees Capture Legal Rules: e.g., the rules for Compensation underthe National Vaccine Injury Compensation Program

  8. Video (screen cam) ofMaking an Implication Tree • Using the Apprentice Decision ModelerTM • Which Employs a Microsoft VisioTM User Interface Play Video 1

  9. II. Evidence Evaluation The General Challenge in Law: • Applying RulestoParticular Cases • Utilizing a GreatVariety of Evidence • Lay witness testimony • Expert witness testimony • Documents • Test results, medical records, etc. • Leaving Flexibility to the Discretion of theFactfinder • Sharing Decision-Making with Judge

  10. II. Evidence Evaluation Modeled in Default-Logic Framework: • Evidentiary AssertionshavingMany-Valued Plausibility-Values • Plausibility Schemas Organize Evidentiary Assertions into Default Patterns of Reasoning • Numerous Plausibility Connectives

  11. Five-Valued Scale True Probably True Undecided Probably False False Seven-Valued Scale Highly Plausible Very Plausible Slightly Plausible Undecided Slightly Implausible Very Implausible Highly Implausible Evidentiary Assertionsare propositions that (e.g.) either are a witness’s testimony or describe other kinds of evidence. They have plausibility-values, e.g.:

  12. Plausibility Schemasare logical patterns of default reasoning,which can be used in a particularcase to prove the issues identified by the legal rules (implication tree). They employplausibility connectivesthat operate on theplausibility-valuesofevidentiary assertions, e.g.: Generalized Conjunction (“MIN”) Generalized Disjunction (“MAX”) Strong Defeater (“REBUT”) Weak Defeater (“UNDERCUT”)

  13. For example, the Statistical-Syllogism Plausibility Schema IF (1) A particular individual is in category A. (2) Most members of category A are also in category B. (3) The members of category A adequately represent that individual with respect to being in B. THEN,that individual is probablyalso in category B.

  14. For example, John is a member of AAAI. Most AAAI members are over age 15. AAAI members adequately represent John with respect to age. Therefore, John is probably over age 15.

  15. The Statistical-Syllogism Schema Modeled in Legal ApprenticeTM , as applied to the Vaccine Injury Act

  16. Analyzing the Reasoning in Particular Cases Apprentice Decision ModelerTM • Implication Trees(Legal Rules) • Plausibility Schemas(Default Logic) • OtherConcepts andStructures(Concepts of parties, evidence, etc.) Legal ApprenticeTM Environment for modeling particular cases (assigning values to subjects, selecting schemas for evidence, etc.) Case Analyses / Reports

  17. Illustration: modeled on Huber v. Sec. of HHS, 22 Cl.Ct. 255 (1991), a compensation case under the National Childhood Vaccine Injury Act of 1986 Petitioner was 5½-year-old boy Received DPT shot at 4 months Suffered seizures within days Developed serious mental disability Preexisting condition: tuberous sclerosis

  18. Subjects within Implication Trees (Rules): “the petitioner” = Adam B. “the vaccination” = the DPT shot on Oct. 5, 2005 “the injury” = the life-long serious mental disabilities Subjects within Plausibility Schemas: “category A” = the category of people who have tuberous sclerosis and suffer seizures before age 2 “category B” = the category of people who have life-long serious mental disabilities Assign Values to Subjects:

  19. The completed schema attached to a terminal proposition of the implication tree for the Vaccine Act

  20. Part of the inference tree forAdam B.’s case,evaluated by the evidence Play Video 2

  21. III. Process-Constrained Decision-Making • Procedural Rules and Decisions • Motions to dismiss • Motion for summary judgment • Directed verdict • Evidentiary Rules and Decisions • Admissibility of evidence • Legal sufficiency of evidence • Standards of proof (eg, preponderance)

  22. III. Process-Constrained Decision-Making Modeled in Default-Logic Framework: • No new logical objects or structures • Legal rules captured by implication trees • Evidentiary assertions apply the rules to the particular case

  23. Emergence of New Rules,and Rules about Rules • Many process decisions areabout adopting, maintaining, or rescinding rules of law – that is, about theshapeof theimplication trees • Important area of research: Modeling thepolicy-based reasoningof courtsabout rules

  24. Emergence of NewPlausibility Schemas • Many process decisions are about how areasonable factfinder goesaboutevaluating evidence – e.g., deciding whether proffered evidence is minimally reliable, or deciding whether a pattern of reasoning is minimally plausible • Important area of research: Developing new plausibility schemas

  25. Some Conclusions • Law is a strategic area for studyingreasoning in multiagent, problem-oriented systems • Numerous complex, important cases • Reasoningextensive,well-documented • Integrates rules, evidence, and policies • Integrates non-expert and expert evidence and reasoning

  26. Conclusions, cont. • Law coordinates many agents working on different aspects of extremely complex cases • Legislatures and regulators • Judges and regulators • Experts and non-experts • Rule-makers and factfinders • Law evolves its own rules, policies, schemas, and logic over time – and does so under (because of ?) the rule of law.

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