[net.ai] Ph.D. Oral - Analogy in Legal Reasoning

KEDAR-CABELLI@RUTGERS.ARPA (07/03/84)

From:  Smadar <KEDAR-CABELLI@RUTGERS.ARPA>

             [Forwarded from the Rutgers bboard by Laws@SRI-AI.]

                    A Ph.D. Oral Examination - Proposal Defense

          Title:    Analogy with Purpose in Legal Reasoning from Precedents
          Speaker:  Smadar Kedar-Cabelli
          Date:     Friday, July 6, 1984, 10:00 - 11:00 am
          Location: Hill Center, room 423

                    Open to DCS Faculty and Students

       One  open  problem in current artificial intelligence (AI) models of
    learning and reasoning by analogy is: which aspects  of  the  analogous
    situations  are  relevant to the analogy, and which are irrelevant?  It
    is currently recognized that analogy involves mapping  some  underlying
    causal  network  of relations between situations [Winston 82], [Gentner
    83], [Burstein 83], [Carbonell 83].  However, most  current  models  of
    analogy  provide  the  system  with  exactly  the  relevant  relations,
    tailor-made to each analogy to be performed.  As AI systems become more
    complex,  we  will  have  to  provide  them  with  the  capability   of
    automatically  focusing  on  the  relevant  aspects  of situations when
    reasoning analogically.  These will have to be sifted  from  the  large
    amount of information used to represent complex, real-world situations.

       In  order  to  study  these  general  issues,  we  are  examining  a
    particular case study of learning and  reasoning  by  analogy:  forming
    legal  concepts  by  legal  reasoning from precedents.  This is studied
    within the TAXMAN II project, which is  investigating  legal  reasoning
    using AI techniques [McCarty 82], [Nagel 83].

       In  this  talk, we will discuss the problem and a proposed solution.
    We examine legal  reasoning  from  precedents  within  the  context  of
    current  AI  models  of  analogy.    We then add a focusing capability.
    Current work on goal-directed learning [Mitchell 83a], [Mitchell  83b],
    and   explanation-based   learning [Dejong   83]   applies   here:  the
    explanation of how the precedent satisfies the intent of the law  (i.e.
    its  goals,  or purposes) helps to automatically focus the reasoning on
    what is relevant.

       Intuitively, suppose a lawyer wishes to argue that a particular case
    involving a bicycle violated  the  following  statute:  'a  vehicle  is
    forbidden  in a public park' [Hart 58].  He might argue by analogy to a
    clear precedent--a passenger car.  He needs to establish that a bicycle
    is a vehicle for the purposes of this statute, that bicycles should  be
    banned from the park for the same reasons that passenger cars are.  The
    purpose,  or  intent  of the law is to prohibit those things that would
    interfere with the serene, quiet setting of the park, or would  destroy
    the  natural  habitat,  and so on.  Reasoning from this, the lawyer can
    determine that aspects of the cases such as the ability to trample over
    lawns,  run  over  small  animals,  make  noise,  are relevant for this
    purpose.  On the other hand, aspects of the cases involving the country
    where the vehicles were manufactured, or the materials the vehicles are
    made of, are irrelevant for this purpose.  Given a  different  law  and
    purpose, these might well be relevant.