TaxSymposium

FRACAS: A ComputerizedAid for Reasoning in TaxVarghese S. JacobThe Ohio State University, Columbus, OH, USA

Joanne H. TurnerUniversity of Michigan Law School, Ann Arbor, MI, USA

Abstract Problem solving in the tax domain requires two kinds of knowledge: of the lawitself and of how the law has been applied in the past. The need for the secondfactor arises as a result of the ambiguity of natural language. The problem solverrequires information on how the courts have adjudicated specific cases in thepast. This information would then provide the basis for reasoning about thecurrent case. In this paper we address the issue of developing a system whichwill retrieve relevant historical cases. The cases are stored using a framerepresentation scheme and the users can retrieve cases by specifying eitherattributes alone or attributes and values associated with them. Currently thesystem has been implemented in Pascal on a Cray. The case base contains 250cases relating to Section 183 of the tax code.

Introduction

Problem solving in the tax domain requiresknowledge on two levels. On one level theproblem solver must have knowledge of thelaw itself. That is, the problem solver mustknow (or be able to find out) what typesof income are taxable, which expenses aredeductible, and so on. For example, Section 162of the Internal Revenue Code specifies that'There shall be allowed as a deduction allthe ordinary and necessary expenses paid orincurred during the taxable year in carrying onany trade or business'. However, because ofthe ambiguity of the natural language in whichtax laws are written, knowledge of the legalterms alone is not enough. In addition toknowing that expenses incurred in a trade orbusiness are deductible, the problem solvermust know (or be able to find out) what makesa particular endeavor a 'trade or business'.

The judicial system operates under the doc­trine of stare decisis, meaning 'let the decisionstand'. Under this policy, courts generally abideby or adhere to previously decided cases. As aresult, the current interpretation of ambiguous

1055-615X/92/020103-19$09.50© 1992 by John Wiley & Sons, Ltd.

phrases such as 'ordinary and necessary' is, toa large degree, governed by their interpretationin the past. Therefore, legal problem solvingalso requires knowledge about cases whichhave previously been adjudicated (called'precedents'). Knowledge of either statutorylanguage or precedents alone is not sufficient.

A major feature of problem solving within thelegal domain is the linking of this knowledge tothe case at hand. Previous attempts at linkagehave generalized information from the pre­cedents and the statutory law to form rules,rather than utilizing case knowledge directly.!For example, Hellawell's (1980) CORPTAX sys­tem used IF-THEN deductive logic to determinewhether a stock redemption qualified for favor­able tax treatment under the 'substantiallydisproportionate' provisions of Section 302(b)(2), considering the related attribution rules ofSection 318(a). However, CORPTAX essentially

1 An exception is the TAXMAN II project (McCartyand Sridharan, 1981, 1989), which uses a prototype­and-deformation model to analyze components ofincome.

Received September 1990Revised March 1991

INTELLIGENT SYSTEMS IN ACCOUNTING, FINANCE AND MANAGEMENT VOL. 1: 103-121 (1992)

replicated the statutory provisions of Section302(b), without addressing semantic and defi­nitional issues. A second stream of research isexemplified by Robison (1983), who appliedprobit analysis and stepwise regression todiscern five factors which influence the judg­ment of whether an activity constitutes abusiness or a hobby. While Robison's goal wasnot a rule-based system per se, the factors heisolated could easily form the basis for rules.A third category is the 'expert' system, whichattempts to replicate human expertise in narrowproblem domains. We do not discuss thevarious expert systems in tax as extensivereviews on the topic can be found in Michaelsonand Messier (1987), Brown and Streit (1988) andBrown (1988a).2

Although a rule-based approach is valuable,it has limitations for some users. First, one cangeneralize only if a sufficient number of caseshave been decided in the same manner. Thisnecessarily limits the issues for which a rule­based system can be generated to those thathave been widely litigated. Second, laws changeover time, and rules that were valid in the pastmay not be appropriate in the future. As aresult, rules must be continuously reviewedand updated where necessary. Third, a rule­based system typically does not include enoughor sufficiently detailed rules to incorporate allthe factors which may influence the outcome,such as the court in which a specific case wasdecided or the particular judge who renderedthe decision. This may be deliberate, in orderto simplify the rule structure, or a necessaryresult of the fact that sufficient cases have notbeen decided which incorporate a particularfactor to be able to articulate a rule. Finally, asnoted by Fikes and Kehler (1985), rules areinadequate to define terms. This problem seemsparticularly acute in domains such as tax, wheredisputes over the definition of such terms as'trade or business' comprise the great majorityof cases litigated and disputes over the formof a rule itself are relatively rare.

In this paper we propose an alternativeapproach to the representation of legal knowl-

2 Application of artificial intelligence techniques intax can be found in Brown [1988b]. A comprehensiveannotated bibliography on expert systems inaccounting can be found in Brown [1989].

104

edge and its retrieval. The approach assumesthat the user is going to utilize historical casesas the basis for reasoning about the currentcase. The system, therefore, functions as adecision support system to provide the userwith the appropriate information. Discussionon the case approach to problem solving canbe found in Riesbeck and Schank (1989).

The paper is structured as follows. In thenext section we discuss a frame-based system3

for storing knowledge in the tax domain, anda method for retrieving the information basedon similarities between the current case andthose in the case base. The Appendices containsample interactions with such a system, whichwe call FRACAS (FRAme-based Case AnalysisSystem), which has been implemented on a

.prototype basis. The final section presentsconclusions and possible future research direc­tions in the area.

Development of FRACAS

The development of FRACAS (a FRAme-basedCase Analysis System) was founded on theconcept that a court case is a collection ofattributes. Examples of these attributes includethe fact that each case has a name and citation,was decided on a particular date, by a particularjudge (or judges). Attributes also encompassthe facts of the case, including characteristicsof the parties involved such as age, education,occupation and income. Finally, the reasoningindicated by the judge to determine the out­come forms additional attributes.

There may be several cases which havesimilar attributes, but never would there betwo cases in which all the attributes were

3 The system described here does not model orcapture expertise or the thought processes of ahuman expert. Rather, it focuses on what Holsappleand Whinston (1987) call 'descriptive knowledge',that is, knowledge about the past, present and/oranticipated states of the world. In this respect, itdiffers from such previously developed systemsas Taxadvisor (Michaelsen, 1984) and ExperTAX(Shpilberg et al., 1986). For this reason, we considerthe system a decision aid which supports taxreasoning, rather than an expert system. However,the development and application of expertise toproblem solving in law requires abilities such asthis system supports.

v.s. JACOB AND JH TURNER

I I Attributes* I IUser InterfaceI I

DictionariesSynonyms

Retrieved New

Cases At tri butesNew Cases &

~Synonyms

Case LocationsIPattern Matching I IUPdate componentlComponentSearch

AttributeUpdate Update Case

t TreeAt tribute Base

TreeRetrieved

ICases

I Case Base I : Attribute Tree It

User

Requests

AccessRelevan

Cases

Figure 1 Structure of FRACAS. 'Currently holds listing of the attributes on which the user can search.

identical. 4 (Indeed, if that were so, the secondcase would not have been adjudicated at all.)Given this assumption, the goal of FRACAS isto assess the similarity of the current case underadjudication to those in the case base. In thissection we discuss the structure of the casebase and the search process used to locate andretrieve the relevant cases.

Figure 1 depicts the structure of FRACAS.The system has been implemented on a CrayY-MP supercomputer, and the search procedureis written in Pascal.s Although implementationon a supercomputer may be viewed as overkillfor a prototype with 250 cases, clearly such a

4 An appeal of an earlier case would share manycharacteristics with the lower court case. However,attributes such as citation, date and judge(s) wouldobviously differ. Most importantly, the reasoningadvanced by the appeals court for its decision maydiffer (sometimes radically) from that of the lowercourt.S At the time the project was initiated, the hostcomputer was a Cray X-MP, which supported alimited range of programming languages. Pascal waschosen because it was supported by the Cray, andbecause programming assistance in Pascal wasreadily available.

machine would be necessary for a completeimplementation which took into account thewhole tax code. Additionally, we believe thatfor a system to be truly successful in thetax domain it would need multiple-reasoningschemes. At a minimum it should also includecase-based, analogical and rule-based reason­ing. A combination of the size of the knowledgebase and the reasoning schemes would requirea fast machine for real-time access. The currentresearch, as a first step, seeks to illustrate thefeasibility of utilizing a supercomputer for oneaspect of the problem. An alternative approachwould be to utilize a network of co-operatingprocessors, where each processor in the networkcan be viewed as an expert in a specific areaof the code. A discussion on the networkedapproach as it relates to auditing can be foundin Jacob and Bailey (1991).

The current system has four components:a user interface, a pattern-matching (search)component, an update component and the casebase. Each of these components is describedbelow.

The Case Base

Tax cases form the knowledge base for FRACASand these cases are subclassified by the particu-

FRACAS: A COMPUTERIZED AID FOR REASONING IN TAX 105

lar tax provIsIOn at issue. For convenience,and following common practice, cases aresubclassified by the particular Code section atissue. Thus, cases involving the deductibilityof medical expenses under Code Section 213form one subclass while those relating to thetaxation of dividend income from mutual fundsform another subclass under Code Section 852.Tax cases can be further classified by theparticular court in which the case is adjudicated.For example, all tax cases decided in the USDistrict Courts form one subclass while alldecided by the US Tax Court form another.

Given the class/attribute nature of the prob­lem domain, the case base is organized inframes. A frame is a knowledge-representationtechnique typically used to represent an objector a class of objects. Several expert systemsshells have been developed using frames (seeWaterman, 1986, for a list of these shells).Frames can be organized into taxonomies basedon classes. Within each class, one can describesubclasses or specializations of the more genericclasses. For example, within the class of all taxcourt cases, those involving the deduction forinterest expense form a subclass. These caseshave properties common to all tax cases, suchas the fact that one party in the case is theCommissioner of Internal Revenue or the USGovernment. In addition, they have propertieswhich distinguish them from other types of taxcases. These properties revolve around thefactual matters presented in the case and thereasoning used by the court to reach a decision.

Currently, our system contains cases involv­ing Section 183, which limits the deductibilityof expenses of activities not engaged in forprofit.6 In particular, our system contains caseson the issue of whether an activity constitutesa 'trade or business' (in which losses aredeductible) or simply a hobby (in whi~h lossesare not deductible)? An activity which shows

6 Section 183 falls under Subtitle A (Income Taxes)of Title 26 of the US Code, Chapter 1 (Normal Taxesand Surtaxes), Subchapter B (Computation of TaxableIncome), Part VI (Itemized Deductions for Individ­uals and Corporations).7 Under Section 469(1), enacted in 1986, any tradeor business in which the taxpayer does not 'materiallyparticipate' is classified as a 'passive activity'. Thedeductibility of losses incurred in passive activitiesis severely restricted. However, Section 469 may

106

a profit in three of five consecutive years (twoof seven consecutive years if the activity dealswith horses) is presumed to be a business, apresumption which can be overcome by theIRS.8 On the other hand, an activity whichshows losses in more than two of five consecu­tive years (five of seven consecutive years ifthe activity deals with horses) is presumed tobe a hobby, a presumption which can beovercome by the taxpayer.

In addition to these 'safe harbor' rules, theRegulations accompanying Section 183 detailseveral factors which may be considered indetermining whether an activity is engaged infor profit, such as whether it is conducted in a'businesslike manner', the expertise of thetaxpayer and the time and effort expended bythe taxpayer. In all, nine factors are discussed.These appear in judges' opinions and are facetsor subslots describing the court's opinion\,

Section 183 was chosen as a basis for develop­ment of the prototype for several reasons. First,it is an area of frequent litigation between theIRS and taxpayers. Thus, the development of asystem to aid tax researchers in this area wouldbe of immediate usefulness in practice. Second,it is so frequently litigated primarily because

have little impact on the level of litigation underSection 183, for two reasons. First, before Section469(1) may be applied it must be determined thatthe activity in question is a trade or business. Thatdetermination is made under Section 183. Thus thestatus of taxpayer's activity as either a trade orbusiness or a hobby is a threshold issue which mustbe addressed before Section 469 comes into play.Second, in many cases litigated under Section 183,the taxpayer would meet the 'material participation'standard. Temp. Reg. 1.469-ST outlines seven testwhich may be used to measure material participation.Under the second of those tests, a taxpayer materiallyparticipates in an activity if 'his participation consti­tutes substantially all of the participation in theactivity of all individuals (including nonowners) forthe tax year'. That is, in Section 183 cases it typicallyis the taxpayer's motive for engaging in the activity,not the taxpayer's level of participation, which isthe crucial issue. .8 Prior to 1987, the presumption of a businessrequired a showing of profits in only two of fiveconsecutive years. Cases decided under pre-1987 laware included in the case base because of theirobvious applicability in other respects to currentproblems. A user searching for cases specificallydecided under pre-1987 law may so specify byrestricting the search by year decided.

v.s. JACOB AND JH TURNER

the language of Section 183 is ripe for interpret­ation. (For example, what is a 'businesslikemanner'?) Thus, it would illustrate the abilitiesof the prototype system to link previouslydecided cases to ambiguities in statutory langu­age.

The cases are organized in a hierarchy withgeneric cases at the top of the hierarchy. Thisallows lower-level cases to inherit properties ofthe class, such as default values, from thehigher-level frames. For example, all casesstored in FRACAS within the class of tax casesdecided in US District Courts have the propertythat a jury trial is available. Specific caseswithin the class indicate whether or not a jurywas used.

Lower-level cases can have specializedproperties as well. For example, most courtopinions are written by a single judge. How­ever, on occasion, an opinion is designated'peJ: curiam', indicating no individual judge isresponsible for the decision. If a particular caseis decided per curiam, its case frame so indicates.

Attributes of each case are incorporatedwithin the frame in slots. Since attributes canhave properties of their own, one can alsospecify the properties of slots within subslots,called facets. Because of the particular codesection chosen as the basis for the prototype,some slots in FRACAS incorporate featuresassociated with decisions under Section 183itself, such as the taxpayer's wealth and back­ground. Other slots in FRACAS store thecitation of the case and the name of the judgerendering the opinion. Although every courtcase contains this information, the facetsdescribing these slots are based on the infor­mation in a specific case.

Illustrative Example

Consider the case of Rex B. Foster (32 TCM42 (1973». This case will be used to demonstratethe robust ability of a frame-based system tostore knowledge about a tax case in easilyretrievable fashion. 9 Briefly, Dr Foster and his

9 For purposes of this prototype, cases were codedinto frames by one of the researchers. A standardtemplate was established based on a preliminaryreading of a sample of cases. Frames and slots

FRACAS A COMPUTERIZED AID FOR REASONING IN TAX

wife, Betty, raised Shetland ponies on theirfarm outside Waterloo, Iowa. Though they lostmoney every year from 1955 through 1970, theywere able to convince the Tax Court that theiroperation was engaged in for profit, and thusthe expenses of which were deductible withoutlimit. Thus, they were able to overcome thepresumption that a horse-breeding operationwhich fails to show a profit in two of sevenconsecutive years is not a business.

Our case-based knowledge retrieval systemfor tax cases begins with a frame containingidentifying information about the case itself.The case information frame has several facetsassociated with it, such as its citation, the yearsat issue and in whose favor the case wasdecided (Figure 2).

The linkage properties of frame-based sys­tems become apparent immediately. The Fostercase shares many properties with other casesheard in US Tax Court, such as:

JURY TRIAL? Not availableAPPEALABLE-TO: US Court of Appeals

The Foster case is automatically linked withother Tax Court cases appealable to the EighthCircuit, and with cases already heard in theEighth Circuit. If there are other propertieswhich are common to other cases, those linksare also established when the case is stored.

Next, the system stores information aboutthe Fosters themselves. For example, the Fostersare individual taxpayers filing a joint return,and thus the Foster case frame inherits im­portant default properties from the class ofINDIVIDUALS, such as calendar tax years anda cash accounting ·.ethod. These propertiescan, of course, be overridden by the facts ofthe case itself, but there is no mention to thecontrary in the Foster case.

The case contains a lengthy accounting ofthe receipts and expenses of the Fosters' ponybreeding between 1955 and 1970. Many aspectsof their finances may be of interest to others:the size of their losses, both in absolute dollars

were added to and deleted from this template toaccommodate specific cases. The Foster case waschosen as an illustration because it was particularlythorough in its discussion of the facts of the caseand of the elements of the court's reasortling. Not allcases included in the case base are as complete intheir discussion.

107

FRAME: Rex B. FosterKEMBER OF SUBCLASS: Sec. 162.

SLOT: CASE INFORMATION

TAXPAYER-NAME

CITATIONCCHPHUSPROCEDURE

COURTDECISION-TYPEJURY TRIAL?APPEALABLE -TO

CIRCUITJUDGE

DATE-OF-DECISIONYEARS-AT-ISSUEDECISION-FORBUSINESS-CLAIMED

ANIMAL-TYPE

SLOT: TAXPAYER

TAXPAYER-TYPERETURN-TYPETAX-YEARACCOUNTING METHOD

RESIDENCEYEARS-AT-ISSUETIME-OF-TRIAL

OCCUPATIONPROFESSION

EMPLOYERAGERELATED-EXPERTISERELATED-EDUCATION

SLOT: BUSINESS INCOME

YEARIGROSS RECEIPTS

NET INCOME (LOSS)YEAR2

GROSS RECEIPTSNET INCOME (LOSS)

YEAR10GROSS RECEIPTSOPERATING EXPENSESHAY AND FUELLABORSHOEINGINSURANCE

DEPRECIATIONNET INCOME (LOSS)IF-NEEDED

Figure 2 Frame representation of a sample case.

108

Rex B. FosterBetty C. Foster

32 TCM 42TC Memo 1973-13None(No US citation isavailable for Tax Court Memodecisions)U.S. Tax CourtMemorandumNoU.S. Court of AppealsEighthIrwin1/23/19731964, 1965, 1966, 1967TaxpayerRaising animalsShetland ponies

IndividualJointCalendarCash

Waterloo, IADelray Beach, FLProfessionalDental surgeonFoster and Foster partnership68Qualified judge of Shetland poniesSpecial course in schooling Shetlandponied

1955o(1,974.64)1956800(4404.14)

19644,614.21

1,232.072,644.551,036.50468.58

6,741.31(16,273.47)(Procedure for computing number ofyears of profits/losses)

VS. JACOB AND JH TURNER

SLOT: OTHER INCOME

YEARlAMOUNT

YEAR10AMOUNT

IF-NEEDED

SLOT: JUDGE'S OPINION

BUSINESS·LIKE MANNER?EVIDENCE CITED

EXPERTISE?EVIDENCE- CITED

TIME AND EFFORT?EVIDENCE-CITED

EXPECTATION OF APPRECIATION?FINANCIAL STATUS?

OTHER FACTORS CITED

Figure 2 Continued.

and in relation to the income from Dr Foster'sdental practice; the pattern of losses; the relativeand absolute amounts of depreciation and cashexpenses; even the particular expenses claimed.The researcher may thus inquire as to theexistence of cases under Section 183 in whicha particular pattern of income or losses, orincome or losses of a particular size, occurred.

Searches of this kind are simply not possiblein pure text-based retrieval systems and aredifficult to accommodate in rule-based ones.Consider a tax accountant or attorney whoseclient's horse-breeding operation has failed toshow a profit in two of seven consecutive years,and thus does not qualify for the presumptionof a trade or business. Such a taxpayer has aparticularly heavy burden of proof, because inorder to win, the taxpayer must overcome thepresumption that the activity is not a trade orbusiness. Locating previously decided cases inwhich the taxpayer succeeded in overcomingthe presumption would obviously be helpful.

FRACAS: A COMPUTERIZED AID FOR REASONING IN TAX

195513,725.00

196429,771.27(Procedure for computing size oflosses relative to other income)

Adequate facilitiesParticipation in horse showsAdvertising in trade journalsYesOccasional help of part-time

trainerShipped ponies to another trainerYesPhysical workHours spent by wifeSelectively bred herdIncome from dental practice "ample'rbut not rvery wealthy"

Decline in market for tcolor~

poniesGrowing demand for ~showi poniesOther breeders left businessHealth of taxpayer

However, such a search is impossible to conducton LEXIS or other pure-text-based retrievalsystems because there is no consistent wayused in previous cases to describe such apattern of losses. Consider these examples fromfour horse-breeding cases selected at random:'sustained series of losses', 'absence of profitsduring the taxable years in question', 'pet­itioner's horse-breeding operation incurredlosses for each of the years in question', 'a lossin each and every year'IO

10 As a second example, consider a tax accountantor attorney whose client, a physician, is concernedabout the effect his occupation may have on thechances of winning a dispute with the IRS overexpenses on his horse farm. A LEXIS search usingthe key words 'Sec. 183 and horse w/5 breeding andphysician' retrieved a total of eight cases. However,in one of those cases, the taxpayer's occupation wasnot physician; rather, the taxpayer had reduced hisinvolvement in horse breeding on his physician's

109

Rule-based systems do not necessarily faremuch better. A rule-based system involvingSection 183 may generate the rule that situationsin which the taxpayer showed profits in onlyone year are likely to be decided against thetaxpayer, but would not be able to pinpointspecific cases which are the exception to therule. Similarly, a rule-based system whichindicated specific income levels at which thetaxpayer either prevailed or lost would be likelyto be rejected as overly specific. Indeed, thenumber of exception rules which would haveto be specified in any rule-based system in thetax domain would be large. (An approach tocombat this problem is that taken in theTAXMAN 11 system (McCarty and Sridharan,1980, 1981).) Yet, locating these precedents iskey to the taxpayer's ultimate success.

The court's decision can also be stored in away which permits easy access to specificportions. For example, the system stores thejudge's conclusions (where indicated) as towhether the activity was carried on in a'business-like manner' and any facts the judgementions which guided the determination. Aresearcher can thus use FRACAS to surveysuccessful arguments for the presence of a'business-like manner', or any of the othereight factors indicated in the Regulations asindicative of a trade or business.

A concern in any knowledge-representationsystem is the danger of biasing the searchprocess by attempting to determine in advancewhich aspects of a case are important. This hasbeen a traditional criticism of pre-storage (asopposed to post-storage) analysis (Chandler,1974). However, the inherent flexibility of aframe-based system allows the details as wellas the general principles of a case to be stored.Also, as the above example demonstrates, theinheritance and default properties of framesactually allow more information about a case

instructions. In three other cases the taxpayer wasa physician, but was not involved in horse breeding.LEXIS retrieved the cases because they containedthe statutory language of Section 183, which includesthe phrase 'horse breeding'. In only four of the eightretrieved cases was the desired combination offactors present. Obviously, the search retrieved onlythose cases where the taxpayer's occupation wasdescribed as 'physican', as opposed to dentist orsurgeon or doctor.

110

to be stored than perhaps the case itselfexplicitly mentions.

In addition to efficient storage of information,the hierarchical nature of the stored informationmakes for efficient retrieval of information. Forexample, if a user were interested in casesdealing with Section 183 under subtitle A, thesearch would follow the path indicated by thearrows in Figure 3. This eliminates the necessityfor searching the entire knowledge base untilcases involving Section 183 are found.

To facilitate retrieval, the frames are linkedalong several additional dimensions. One link

U.S. Court Cases

It l

~ Tax cases Other cases

II

Cases involvingOther tax cases

Subtitle A

II I

Cases involving Cases involvingnormal taxes

land surtaxes other chapters

I[ 1

Cases involving Cases Involvingother subchapters Subchapter B

Il lI

Cases involving Cases involvingPart VI:

other parts itemized deductionsI

I I

Cases involvingOther cases

Sec 183

l rRex B. Foster32TCM 42

Figure 3 Organization of case frames on an issuebasis.

V.S. JACOB AND JH TURNER

recognizes that users may be interested inretrieving cases with particular attributes. Forexample, one may be interested only in casesin which the adequacy of the taxpayer's recordkeeping is discussed. The presence or absenceof this attribute differentiates one case fromanother. Another link recognizes that usersmay be interested in retrieving cases with aspecific attribute-value combination. For exam­ple, one may be interested in cases decided bya particular judge. Since all cases are decidedby judges, the feature which distinguishesthese cases is the judge. Cases having attributeswith common value are thus linked, and thelinks are used to retrieve cases based on theuser's needs.

The Pattern-matching Component

This component serves two functions. First,given the attributes which are specified for thecurrent case, it retrieves all those cases whichhave the specified attributes. Second, giventhat there would rarely be a perfect matchbetween the current case and any prior case,the component also generates a measure of thesimilarity betwen each retrieved case and thecurrent case based on the match betweenattributes.

Users may take one of two possibleapproaches to accessing the knowledge base.The first is to search for cases with specificattribute-value combinations. FRACAS con­ducts such a s~arch by following the linksembedded within the frames. The end resultof such a search is only those cases whichhave all the specified attribute-value pairs. Forexample, such a search may produce all TaxCourt cases in the case base in which thetaxpayer's occupation was attorney but wouldnot produce Tax Court cases involving tax­payers in other occupations. A search whichspecified many attribute-value pairs wouldundoubtedly produce relatively fewer cases,but the user could easily query the system withsubsets of attribute-value pairs to see thosecases which were not complete matches.

The second approach assumes the user isinterested in viewing cases with certain attri­butes, regardless of the value of the attribute.For example, the user may be interested inretrieving all cases which indicated the tax­payer's occupation or discussed the impact of

FRACAS: A COMPUTERIZED AID FOR REASONING IN TAX

the taxpayer's occupation on the ultimate result,without restricting the search to a specificoccupation.ll Once the user has specified theattributes of interest, the system uses this as abasis for retrieving cases using an attributetree, rather than using the cases directly.

The attribute tree contains less informationthan the case hierarchy, thus manipulating thetree is much easier than manipulating the setof cases itself. The nodes of the attributetree contain differentiating attributes, that is,attributes which make a set of cases uniquelydifferent from another set of cases. The tree isorganized such that each parent has all thecommon attributes of the children, and siblingsdiffer from each other by at least one additionalattributeY Each node is linked to the one (ormore) frame(s) which has all the attributesspecified at the node. The search for attributesprogresses by searching the attribute tree, andthen the frames composing the casesP

Since the likelihood of finding an exact matchbetween the user-specified attributes and aspecific case is low, particularly as the numberof specified attributes increases, the user canspecify on a scale of 1 to 10 the importance of

11 It is relatively common in cases under Section 183for the court to discuss the taxpayer's occupation.Courts often cite the taxpayer's occupation as evi­dence that the activity was not undertaken for thepurpose of sheltering income. For example, in Comm.v. Yancy (48 TCM 872) the taxpayer worked two full­time jobs (truck driver and steelworker). The courtnoted, 'Petitioners engaged in their horse-racing andhorse-breeding activities in order to make profits soas to enable them to raise their economic standardof living . . . Petitioners did not have other wealthto fall back on.' Courts apparently also believe thatcertain occupations are so time consuming as toleave the taxpayer little or no time to devote to the'business'. In Comm. v. Joint Implant Surgeons,Inc. (56 TCM 799), following a description of thetaxpayer's typical workday, the court observed: 'Withsuch constraints on his time as mentioned in hisdescription, it would appear near impossible for himto take part in any daily activities associated withthe farm's operation: Finally: courts may simplybelieve that taxpayer in certain occupations are morelikely to turn to such 'businesses' as farming andhorse breeding, which can generate substantial paperlosses, in order to shelter income from other sources.12 For example, parent A and children Band C shareattributes 1 through 5. Child B also has attribute 6,while child B has attribute 7.13 The attribute tree is not imbedded in the frames.

111

each attribute in the search as well as the levelof match required. Cases which do not fallwithin these user-specified bounds are dis­carded. The similarity between the retrievedcases and the current case is based on thenumber of matching attributes. Specifically, thesimilarity score is the ratio of the sum of theuser-assigned values of attributes successfullymatched to the total weights indicated by theuser, and thus ranges from °to 1. In the currentframework a case which has more than thenumber of attributes specified by the user willbe assumed to be similar to that of the user.

The Update Component

The update component automatically updatesthe search tree as new cases are added to theknowledge base. Once the attributes of the newcase are specified by the user, the systemautomatically determines its position in thesearch tree and establishes links between thenew case and other related cases in the knowl­edge base. The update component also updatesthe dictionary. Appendix 2 contains a part ofa sample interaction between the user and theupdate component.

The User Interface

As the appendices illustrate, the user interfaceis set up in menu fashion. The first screendisplays the user instructions and the mainmenu from which the user may choose severalactions: view a dictionary, conduct an attribute­based search, conduct a search for attribute­value pairs, update the knowledge base or exitthe program.

The system includes two dictionaries. Onelists all the attributes which differentiate thecases themselves, while the other lists thoseattributes on which an attribute-value searchmay be conducted. Examples of differentiatingattributes include whether the taxpayer main­tained 'acceptable' records, whether the tax­payer devoted much time to the activity andwhether persons other than the taxpayer test­ified on the taxpayer's behalf in court. Theprimary purpose of the dictionaries is toacquaint the user with the attributes presentin the knowledge base. Future expansion of thedictionary would involve specifying synonyms,so that the user is not constrained to the specific

112

terminology used in the knowledge base.Examples of attributes on which an attribute-­value search may be conducted include tax­payer's name, citation, name of the judgerendering the decision and specific years atissue.

Choosing the attribute search option allowsthe user to retrieve cases with particular attri­butes. The user has the option of ranking theimportance of the attributes on a scale of 1 to10, with 10 being the most important. If theuser chooses not to enter any weights, thesystem automatically assigns equal weights tothe specified attributes.

Once the user has entered the attributes, thesystem offers the user the option to perform asearch for specific values among retrieved cases.If this option is chosen, the user is requestedto enter desired values for the attributes.Finally, the user is asked to specify maximumand minimum percentages of attributes speci­fied to be retrieved. The higher the maximumvalue specified, the more restrictive the searchwill be and the lower the minimum value, themore inclusive the search.

The system displays the cases retrieved, withthe highest degrees of matching displayed first.Since cases could have more attributes thanthose specified by the user, the user is firstshown the differentiating attributes in thecase(s) matched. Next, an excerpt from the firstcase is displayed (see Appendix 1). If a valuesearch was specified, the excerpt indicates anypositive results of the value search. At thispoint, the user is shown a menu from whichhe or she may choose to see the frame of thecase excerpted, go on to the next case havingthe attributes specified, go on to the next setof cases which have a different set of attributesor return to the main menu.

The third option on the main menu is thevalue search. If this option is chosen, the useris asked to specify attribute-value pairs. Asbefore, the system displays frames of caseswhich match the specifications, as well as theparticular combinations matched. The user mayview frames of the matched cases, continueaccessing cases or return to the main menu.

Finally, the option for updating the knowl­edge base allows the user to enterattribute-value pairs for new cases. If thesystem does not recognize an attribute, theuser may view the dictionary to select an

V.S. JACOB AND JH TURNER

existing description for the attribute or add thenew description to the dictionary. Before theentry is finalized the system displays theinformation entered and gives the user theopportunity to make changes.

Sample Interaction with FRACAS

Appendix I shows a sample interaction betweenthe user and the system. The user in thissession is interested in finding previous caseswith six attributes, and identifies the mostimportant characteristics of these previous casesto be that the taxpayer in question (1) experi­enced extraordinary problems, (2) had a largeamount of outside (nonfarm) income and (3)used the knowledge gained from consultationwith others to improve business operations.The user identifies of somewhat lesser import­ance the fact that the taxpayer in questionexperienced relatively small business losses.Finally, the user indicates an interest in pre­vious cases involving animal breeding in whichthe judge determined that the taxpayer hadacted in a business-like manner.

The system returns with a score whichindicates how close the attributes in the caseare to those specified in the search. 14 There isno penalty if the retrieved case possessesmore attributes than were specified. Since theattribute tree is stored in arrays, the index inan attribute search denotes the location atwhich the set of attributes begins. An index isalso used to denote the location of a particularcase in the case base. (The index was primarilyset up for debugging purposes.)

The system also returns with a list of differen­tiating attributes. That is, the retrieved casespossess these attributes in addition to thoserequested in the search. The first case recalledpossesses all the requested attributes. Notethat in determining that the Engdahls hadexperienced extraordinary problems, the judgecited four factors: a change in the market forsaddlebred horses, illness among their horses,the deaths of two horses and the fact that the

14 The matching score is calculated as the sum ofthe assigned values of all achieved matches dividedby the total weight possible if all matches wereachieved. If all possible matches are achieved, thematching score equals 1.

FRACAS A COMPUTERIZED AID FOR REASONING IN TAX

trainer hired by the Engdahls failed to devotehis best efforts to the task. The judge indicatedthat the Engdahls had used the knowledgelearned to improve their operations, but gaveno specifics. On the other hand, the judge citedfour separate facts in determining that theEngdhals had acted in a business-like manner:although they did not maintain a separate bankaccount for their horse operations, they didkeep a separate ledger for their horse-relatedexpenses and checked monthly variations intheir expenses. They also advertised and exhib­ited their horses.

Based on the Engdahl case, the user nowhas concrete examples of situations whichconstitute 'extraordinary problems' in the lang­uage of the Regulations. A horse breeder whohas experienced one or more of these factorscan argue that they too have had extraordinaryproblems and that the fact that they havesuffered persistent losses should not preventtheir activities from being considered a tradeor business. A taxpayer in some activity otherthan horse breeding or who experienced someother problem can use these examples to argueby analogy. Similarly, the Engdahl case givesfour concrete examples of what constitutes a'business-like manner', which may be used tosupport an argument for classifying an activityas a trade or business.

Conclusions

This paper discusses a decision-support systemwhich may be used to retrieve historical courtcases relevant to a particular current legalproblem. As compared to traditional uses for adecision-support system, practitioners in legal­related domains are more typically interestedin retrieving historical cases than in modellingissues. We have developed a system whichallows the user to access relevant cases easilybased on a comparison of desired attributes orvalues of the attributes.

To develop a complete decision-support sys­tem for legal reasoning will obviously requireconsiderable effort. One of the most labor­intensive parts of such a developmental effortis obviously the coding of cases into frames.In the absence of standardization, decisionsabout the type and number of slots for aparticular case must be made (as in this

113

prototype) by the system developer, workingin conjuction with experts in the area. We notethe many parallels between this effort and themethods used in developing and maintainingthe West Digest system.15 Further research is

also required to enhance the developed systemto take into account such features as thegeneration of rules (where appropriate) andother modes of reasoning.

Appendix 1: A Sample Interaction with FRACAS

SOME MORE HELPFUL HINTS:WHEN SPECIFYING A DATE USE TWO DIGITS AND SLASHES

EXAMPLES: 78, 06/78, 06/15/78DO NOT USE $ SIGNS IN AMOUNTS. EXAMPLE: 9,999.992.

WELCOME TO FRACAS. THROUGH THIS PROGRAM THE USER MAY ACCESS ADATABASE OF TAX CASES. THE INFORMATION CONTAINED IN THESE CASESHAS BEEN REDUCED TO A SERIES OF ATTRIBUTES AND VALUES - EXAMPLE:JUDGE/SMITH. THE CASES HAVE BEEN ORGANIZED BY PRIMARYDIFFERENTIATING ATTRIBUTES. CASES CAN BE RETRIEVED BY MATCHING ASERIES OF ATTRIBUTES TO THE DATABASE. WHEN USING THIS PRIMARYATTRIBUTE SEARCH, THE USER MAY ALSO SPECIFY THE VALUES HE ISINTERESTED IN FOR UP TO 10 ATTRIBUTES. FOR EXAMPLE, A USER MAYWISH TO SEARCH THE DATABASE BASES ON THE EXISTENct OF THREEATTRIBUTED - INCOME, PROPERTY, AND BACKGROUND. AT THE SAME TIMETHE USER MAY NEED TO RECOGNIZE WHICH OF THE MANY CASES WITH THEABOVE ATTRIBUTES HAS THE VALUE SMITH FOR THE ATTRIBUTE JUDGE ANDTHE VALUE OF $30,000 FOR INCOME. THE ABOVE IS THE PREFERREdSEARCH.

THE DATABASE MAY ALSO BE SEARCHED SOLELY ON ATTRIBUTE/VALUECOMBINATIONS BY CHOOSING VALUE SEARCH FROM THE MAIN MENU. FOREXAMPLE, ALL CASES WITH JUDGE/SMITH TYPE/HORSEBREEDING.

ENTER FOR MORETWO DICTIONARIES OF TERMS ARE PROVIDED FOR USE WITH THIS

PROGRAM. THE FIRST CONTAINS PRIMARY DIFFERENTIA'l1ING ATTRIBUTES TOBE USED IN THE ATTRIBUTE SEARCH. THE SECOND CONTAINS A LISTING OFATTRIBUTES WHOSE VALUES MAY BE OF INTEREST EITHER AS THE SECONDARYSTEP TO THE ATTRIBUTE MUST BE ENTERED EXACTLY AS THEY APPEAR IN THEDICTIONARIES.

HERE ARE1.

NOW, YOU MAY1. LOOK AT A DICTIONARY2. SEARCH BASED ON ATTRIBUTES3 . SEARCH BASED ON VALUES4. UPDATE THE DATABASE5. QUIT

ENTER NUMBER1WHICH DICTIONARY DO YOU NEED?

1. PRIMARY ATTRIBUTES2. VALUE SEARCH ATTRIBUTES

ENTER NUMBER1ACCEPTABLE RECORDSACT COST-CONSCIOUS WAYACTINGBACKGROUNDBREEDINGBUSINESS-LIKE MANNERCASE INFOCPA OR ATTORNEY IN COURTDEVOTE MUCH TIME TO FARMDISCONTINUE POOR METHODSEMPLOY COMPETENT LABOREMPLOY TENANT FARMEREXPAND FARMING OPERATIONEXPECT PROP APPRECIATEEXPECT OF APPRECIATIONEXPERTISEEXTRAORDINARY PROBLEMSFARM HAVE FORMAL BUS NAMEFARM IN BUS-LIKE MANNER

************************** Exa~ples of attrlbutes** stored in FRACAS •************ ******* *** ** *

15 The West Digest system parses cases based onelements of the reasoning used in deciding theoutcome. Cases are then catalogued according to adetailed outlIne of 'key numbers', which representa permanent or fixed number given to a specificpoint of case law. The process of parsing cases is

described as follows. 'Every point of law dealt within the case is carefully noted by the editor, whothen writes a headnote for each point ... A casewill have as many headnotes as there are points oflaw in the opinion ... The Key Number is assignedby an attorney editor who is an expert in the fieldof law governed by the assigned topic' (West, 1988).

114 V.S. JACOB AND JH TURNER

*******••********************************* The user selects six attributes to be ** investigated and assigns a level of ** importance to each ******************************************

FARM-RELATED ASSOCIATIONSENTER FOR MORE

FARMINGFINANCESFINANCIAL STATUSHAVE A PROFIT PLANHAVE EXPERIENCEHIST. OF INCOME & LOSSESIMPROVEMENTSINCOMEINVESTMENTLEASINGLEAST ONE YEAR OF INCOMELIVE ON OR NEAR THE FARMLOSSES IN SIMILAR BUS.LOSSES ONLY FIRST 5 YRSLOSSES OVER LONG PERIODMEMBERSHIPSMUCH NONFARM INCOMEMUCH NONFARM NET ASSETSMUCH PLEASURE FROM FARMMUCH TAX SAVINGS

ENTER FOR MORENONFARM-RELATED REC.OCCASIONAL PROFITSOPERATE LIKE PROFIT BUS.OTHER POSSIBLE CASESOTHERS TESTIFY IN COURTPERSONAL PLEASUREPERSONALLY TESTIFYPROPERTYPUBLISHINGQUIT ANOTHER OCCUPATIONRACINGREAL ESTATERENTALREPORT APPROP. EXPENSESRESEARCHSALESSERVICESMALL NET LOSSESSOME SUBSTANTIAL INCOMESPECULATIVE-TYPE FARM

ENTER FOR MORESUCCESS IN SIMILAR BUS.SUCCESS OTHER ACTIVITIESTAXPAYERTERMINATE FARM OPERATIONSTIME AND EFFORTUSE KNOWLEDGE LEARNED

ENTER FOR MORE

WOULD YOU LIKE TO SEE ANOTHER DICTIONARYENTER Y OR NN

NOW, YOU MAY1. LOOK AT A DICTIONARY2. SEARCH BASED ON ATTRIBUTES3. SEARCH BASED ON VALUES4. UPDATE THE DATABASES5. QUIT

ENTER NUMBER2YOU ARE USING AN ATTRIBUTE SEARCHWOULD YOU LIKE TO RANK THE IMPORTANCE OF THE APPEARANCE OFATTRIBUTESENTER Y OR NYENTER PRIMARY ATTRIBUTES, ONE PER LINE:AND ENTER A RANK FROM 1 TO 10 FOR EACH ATTRIBUTE ON THE NEXT LINEENTER '.' WHEN FINISHED'CASE INFO' AND 'OTHER POSSIBLE CASES' ARE GIVEN ATTRIBUTES?BREEDING?? 4?EMPLOY TENANT FARMER1? 8?EXTRAORDINARY PROBLEMS?? 10?FARM IN BUS-LIKE MANNER?? 3

FRACAS: A COMPUTERIZED AID FOR REASONING IN TAX 115

?SMALL NET LOSSES?? 6?MUCH NONFARM INCOME?? 10?USE KNOWLEDGE LEARNED?? 10?*

WOULD YOU LIKE TO DO A VALUE SEARCH: YIN?N

SEARCHING FOR SIMILAR CASES . . .

**************************************

* The retrieved set of cases possess ** these attributes ***************************************

44 WITH A MATCH SCORE OF:

GIVE MINIMUM PERCENT MATCH YOU WISH TO VIEW.50

GIVE MAXIMUM PERCENT MATCH YOU WISH TO VIEW.100

THESE ATTRIBUTES ARE INDEXED AT:CASE INFOBREEDINGTAXPAYERPROPERTYIMPROVEMENTSFARM IN BUS-LIKE MANNERACCEPTABLE RECORDSDISCONTINUE POOR METHODSHAVE EXPERIENCEUSE KNOWLEDGE LEARNEDDEVOTE MUCH TIME TO FARMLIVE ON OR NEAR THE FARMWORK ON THE FARMEMPLOY TENANT FARMEREXPECT PROP APPRECIATELOSSES OVER LONG PERIODEXTRAORDINARY PROBLEMSMUCH NONFARM INCOMEMUCH TAX SAVINGSEXPAND FARMING OPERATIONCPA OR ATTORNEY IN COURTTERMINATE FARM OPERATIONSFARM-RELATED ASSOCIATIONSOTHER POSSIBLE CASES

EX 50

EX 100

0.882

ENTER FOR CASES

THE CASE WITH THE ABOVE ATTRIBUTES ARE:

************************************************************* THIS CASE'S INDEX IN THE CASE TREE IS: 2430 ** ** TAXPAYER: THEODORE N ENGDAHL* TAXPAYER: ADELINE M ENGDAHL ** CITATION ** US 72 TC 659 ********.****************************************************

NOW YOU MAY:1. LOOK AT THE ABOVE CASE2. CONTINUE TO ACCESS CASES3. SKIP TO NEXT GROUP OF ATTRIBUTES4 • RETURN TO MAIN MENU

ENTER NUMBER1

116

CASE INFOCASE NAMECASE NAMECITATIONUSCOURTDECISION TYPEDOCKET NODATE OF DECISIONJUDGEYEARSYEARSYEARSDECISION FORBUSINESS CLAIMEDBREEDINGTAXPAYERRESIDENCEOCCUPATION

ENTER FOR MORE

*EMPLOYER

THEODORE N ENGDAHLADELINE M ENGDAHL

72 TC 659USUS TAX COURTREGULAR9912-7507/11/79HALL717273TAXPAYERBREEDINGHORSE

SANTA CLARA CTY, CAORTHODONTIST

THEODORESELF

v.s. JACOB AND JH TURNER

OCCUPATION

*EMPLOYERHOURS WORKEDPROPERTYLOCATIONNO. OF ACRESUSED AS RESIDENCEACRES USED AS RESIDENCEEXISTING STRUCTURESLARGER THAN PREVIOUSIMPROVEMENTSDATE OFNO. OF STALLSIMPROVEMENTSDATE OFSIZE OF

DIRECTORTHEODORECA. SADDLE HORSE BREEDERS ASSO35-55 HRS/WK

MORGAN HILL, CA2.5YES.5HOUSENOSTABLENOT REPORTED7 CONVERTED TO 12TACK ROOMNOT REPORTED7 - 8 TONS OF HAY

BEST EFFORTSYESYESYESYESYESYESCALIFORNIA SADDLE BREEDERS ASSOCNONONONONONONONONONO

BOUGHT RANCH TO REDUCE EXPENSEDISPOSED OF UNSUCCESSFUL HORSEFIRED TRAINERYESINFORMAL AND CONTINUOUS CONSULYESYESYESYESYESPROFESSIONAL TRAINERYESYES64-75YESCHANGE IN MARKET FOR SADDLE­BRED HORSES, MEDICAL PROBLEMSWITH HORSES, 2 HORSES DIED,TRAINER FAILED TO DEVOTE HIS

FENCENOT REPORTEDSELFHOLDING CORALNOT REPORTEDIRRIGATION SYSTEMNOT REPORTEDSELFPASTURENOT REPORTEDYESNO SEPARATE ACCOUNT FOR FARMCHECKED MONTHLY VARIATIONS INEXPENSES, ADVERTISED AND EXHIBHORSES, SEPARATE LEDGER FORHORSE OPERATIONYESSEPARATE LEDGER FOR HORSE OPERYES

ENTER FOR MOREIMPROVEMENTSDATE OFWORK DONE BYIMPROVEMENTSDATE OFIMPROVEMENTSDATE OFWORK DONE BYIMPROVEMENTSDATE OFFARM IN BUS-LIKE MANNER

*****ACCEPTABLE RECORDS*DISCONTINUE POOR METHODS

ENTER FOR MORE

***HAVE EXPERIENCE

*USE KNOWLEDGE LEARNEDDEVOTE MUCH TIME TO FARMLIVE ON OR NEAR THE FARMWORK ON THE FARMEMPLOY TENANT FARMER

*EXPECT PROP APPRECIATELOSSES OVER LONG PERIOD

*EXTRAORDINARY PROBLEMS

****

ENTER FOR MORE

*MUCH NONFARM INCOMEMUCH TAX SAVINGSEXPAND FARMING OPERATIONCPA OR ATTORNEY IN COURTTERMINATE FARM OPERATIONSFARM-RELATED ASSOCIATION

*QUIT ANOTHER OCCUPATIONHAVE EXPERIENCESUCCESS IN SIMILAR BUS.LOSSES IN SIMILAR BUS.LEAST ONE YEAR OF INCOMESOME SUBSTANTIAL INCOMESMALL NET LOSSESMUCH NONFARM NET ASSETSMUCH PLEASURE FROM FARMNONFARM-RELATED REC.

ENTER FOR MOREOPERATE LIKE PROFIT BUS.FAMILY WORK ON THE FARMEMPLOY COMPETENT LABORSPECULATIVE-TYPE FARMHAVE A PROFIT PLANREPORT APPROP. EXPENSESACT COST-CONSCIOUS WAYPERSONALLY TESTIFY

NOT MENTIONEDNOT MENTIONEDNOT MENTIONEDNOT MENTIONEDNOT MENTIONEDNOT MENTIONEDNOT MENTIONEDNOT MENTIONED

******************************* No conclusions about these ** factors coul d be drawn ** from the case *******************************

FRACAS: A COMPUTERIZED AID FOR REASONING IN TAX 117

OTHERS TESTIFY IN COURTFARM HAVE FORMAL BUS NAMEOTHER POSSIBLE CASES

NOT MENTIONEDNOT MENTIONED

THESE ATTRIBUTES ARE INDEXED AT:CASE INFOBREEDINGTAXPAYERPROPERTYACCEPTABLE RECORDSHAVE EXPERIENCEDEVOTE MUCH TIME TO FARMLIVE ON OR NEAR THE FARMWORK ON THE FARMEMPLOY TENANT FARMEREMPLOY COMPETENT LABORLOSSES OVER LONG PERIODEXTRAORDINARY PROBLEMSLEAST ONE YEAR OF INCOMESMALL NET LOSSESMUCH NONFARM INCOMEMUCH TAX SAVINGSMUCH PLEASURE FROM FARMREPORT APPROP. EXPENSESPERSONALLY TESTIFYOTHERS TESTIFY IN COURTCPA OR ATTORNEY IN COURTFARM-RELATED ASSOCIATIONSFARM HAVE FORMAL BUS NAMEOTHER POSSIBLE CASES

ENTER FOR CASES

21 WITH A MATCH SCORE OF: 0.745

***************************** Attrtbutes with the next ** highest match *****************************

THE CASES WITH THE ABOVE ATTRIBUTES ARE:

****************************************************** ••• **.* THIS CASE'S INDEX IN THE CASE TREE IS: 973 ** ** TAXPAYER: HUNTER FAULCONER SR ** TAXPAYER: MARY T FAULCONER ** CITATION ** PH 83165 PH MEMO TC *.**********•••••***.**••***************••*****••••••********

NOW YOU MAY:1. LOOK AT THE ABOVE CASE2. CONTINUE TO ACCESS CASES3 • SKIP TO NEXT GROUP OF ATTRIBUTES4 • RETURN TO MAIN MENU

ENTER NUMBER4

NOW, YOU MAY1. LOOK AT ANY ONE OF THE RETURNED CASES2. SEE CASE WITH A MATCH SCORE < YOUR MINIMUM3 • RETURN TO 1ST MENU

ENTER NUMBER3

NOW, YOU MAY1. LOOK AT A DICTIONARY2. SEARCH BASED ON ATTRIBUTES3. SEARCH BASED ON VALUES4 • UPDATE THE DATABASE5. QUIT

ENTER NUMBER3YOU HAVE CHOSEN TO DO A VALUESEARCHPRESENTLY, THIS SEARCH WORKS BEST WHEN ONLY A' FEW ATTRIBUTE/VALUE COMBINATIONS ARE LOOKED FOR AT ONE TIME. THIS ISBECAUSE ONLY THOSE CASES THAT MATCH ALL ATTRIBUTE/VALUECOMBINATIONS BEING SEARCH FOR ARE RETURNED. TWO OR THREECOMBINATIONS AT ONCE IS RECOMMENDED. TEN IS THE MAXIMUM

118

ENTER THE ATTRIBUTES AND THENBELOW. EXAMPLE: JUDGE

SMITHENTER '*' WHEN .FINISHED?JUDGE??NIMS?YEARS??76?*

THE DESIRED VALUE ON THE LINE

***************************************** The user is searching for all cases ** decided by Judge Nims in whi ch the ** tax year at issue was 1976. Note* that the case itself was decided in* 1986. *****************************************

V.S JACOB AND JH TURNER

CASE INFO SHOULD BE THE FIRSTATTRIBUTE. ALL OTHER •ATTRIBUTES SHOULD BE PRECEDED

CASES WHOSE VALUES MATCH THE GIVEN:

************************************************************* THIS CASE'S INDEX IN THE CASE TREE IS: 4187 ** ** TAXPAYER: KATHLEEN M. JENNY ** TAXPAYER: JULIUS R. JENNY ** CITATION ** PH 83,001 PH MEMO TC ** JUDGE NIMS ** YEARS 76 *.*.*********************************************************

Appendix 2: The Update Component of FRACAS

NOW, YOU MAY1. LOOK AT A DICTIONARY2. SEARCH BASED ON ATTRIBUTES3 • SEARCH BASED ON VALUES4. UPDATE THE DATABASE5. QUIT

ENTER NUMBER4YOU HAVE CHOSEN TO UPDATE THE DATABASE. IT IS ASSUMED THATTHE NEW CASE IS CURRENTLY IN ATTRIBUTE/VALUE FORM AND THATTHE SIGNIFICANT DIFFERENTIATING ATTRIBUTES ARE KNOWN

TO ENTER THE CASE, SIMPLY ENTER THE ATTRIBUTE AND THEN ITSVALUE ON THE LINE BELOW. IF AN ATTRIBUTE DOES NOT HAVE AVALUE, ENTER A BLANK LINE.SIGNIFICANT DIFFERENTIATINGSIGNIFICANT DIFFERENTIATINGBY THE LINES$$AND A .. , .. SHOULD BE ENTERED TO SIGNIFY THE END OF THE CASEFOR EXAMPLE:CASE INFO

JUDGESMITH$$BREEDINGHORSES,PLEASE BEGIN

?CASE INFO???JUDGE??SMITH?$??$?BREEDING??COW?$??$?TAXPAYER?1?$??$?EXTRAORDINARY PROBLEMS??YES?EVIDENCE??TWO STERILE BULLS?$??$?FARM IN BUS-LIKE MANNER??YES?$??$?MUCH NONFARM INCOME??YES?AMOUNT???$??$?MUCH PLEASURE FROM FARM??YES?$??$

FRACAS: A COMPUTERIZED AID FOR REASONING IN TAX 119

?SMALL NET LOSSES??YES?AMOUNT??6000?$??$?USE KNOWLEDGE LEARNED???YES??YES?$??$?OTHER POSSIBLE CASES???#??#

YES

SMITHCOW

YESYES6000

YESTWO STERILE BULLSYESYES

8676 CASE INFO8677 JUDGE8678 BREEDING8679 TAXPAYER8680 EXTRAORDINARY PROBLEMS8681 EVIDENCE8682 FARM IN BUS-LIKE MANNER8683 MUCH NONFARM INCOME8684 AMOUNT8685 MUCH PLEASURE FROM FARM8686 SMALL NET LOSSES8687 AMOUNT8688 USE KNOWLEDGE LEARNED8689 YES8690 OTHER POSSIBLE CASES

TO MAKE CORRECTIONS ENTER LINE NUMBER: IF OK ENTER 0TO START OVER ENTER 1

8684ENTER 1 TO CHANGE ATTRIBUTE, 2 TO CHANGE VALUE? 2ENTER CORRECT VALUE?YESENTER Y FOR MORE CORRECTIONS AND N TO CONTINUE UPDATEY

TO MAKE CORRECTIONS ENTER LINE NUMBER: IF OK ENTER 0TO START OVER ENTER 1

8688ENTER 1 TO CHANGE ATTRIBUTE, 2 TO CHANGE VALUE? 2ENTER CORRECT VALUE?YESENTER Y FOR MORE CORRECTIONS AND N TO CONTINUE UPDATE

ENTERING CASE

YOUR CASE HAS BEEN ENTERED AND YOU ARE BEING RETURNED TO THE MAINMENU

NOW, YOU MAY1. LOOK AT A DICTIONARY2. SEARCH BASED ON ATTRIBUTES3 • SEARCH BASED ON VALUES4 . UPDATE THE DATABASE5. QUIT

ENTER NUMBER5

Acknowledgements References

This work was begun while both authors weremembers of the Faculty of Accounting and MISat The Ohio State University. The project hasbeen supported in part by grants from theArthur Young Foundation, under its TaxResearch Grant Program, and from the OhioSupercomputing Center. The programmingassistance of Kathy Vender is gratefullyacknowledged.

Brown, CE. and I.K. Streit (1988) 'A survey of taxexpert systems', Expert Systems Review, 1, No.2,6-12.

Brown, CE. (1988a) 'Tax expert systems in industryand accounting', Expert Systems Review, 1, No.3,3-8.

Brown, CE. (ed.) (1988b) 'AI application in taxation',Expert Systems Review, I, No.4, 3-16.

Brown, C.E. (1989) 'Accounting expert systems: acomprehensive, annotated bibliography', ExpertSystems Review, 2, No. I, 2, 23-129.

120 v.s. JACOB AND JH TURNER

Chandler, J.P. (1974) 'Computers and case law',Rutgers Journal of Computers and the Law, 3, No.2, 202-18.

Fikes, R. and Kehler T. (1985) 'The role of frame-basedrepresentation in reasoning', Communications ofthe ACM, September, 904-20.

Hellawell, R (1980) 'A computer program for legalplanning and analysis: taxation of stock redemp­tions', Columbia Law Review, 80, No.7, 1363-98.

Holsapple, C.W. and Whinston, A.B. (1987) 'Knowl­edge-based organizations', The Information Society,5,77-90.

Jacob, V.5. and Bailey, A.D. (1991) 'A conceptualframework for the network approach to expertsystems development in auditing', InformationProcessing and Management (forthcoming).

McCarty, L.T. and Sridharan, N.S. (1980) 'The rep­resentation of an evolving system of legal concepts:I. Logical templates', Proceedings, Third BiennialConference of the Canadian Society for ComputationalStudies of Intelligence, pp. 304-11.

McCarty, L.T. and Sridharan, N.S. (1981) 'The rep-

FRACAS: A COMPUTERIZED AID FOR REASONING IN TAX

resentation of an evolving system of legal concepts:II. Prototypes and deformations', Proceedings, Sev­enth International Joint Conference on ArtificialIntelligence, Vancouver, pp. 246-53.

Michaelsen, RH. (1984) 'An expert system for federaltax planning', Expert Systems, October, 149~7.

Michaelsen, R H. and Messier, W.F. (1987) 'Expertsystems in taxation', Journal of the American TaxAssociation, 8, No.2, 7-21.

Riesbeck, C. K. and Schank, R.C. (1989) Inside Case­Based Reasoning, Hillsdale, NJ: Erlbaum.

Robison, J. (1983) 'Tax court classification of activitiesnot engaged in for profit: some empirical evidence',Journal of the American Taxation Association, Fall,7-22.

Shpilberg, D., Graham, L.E. and Schatz, H. (1986)'ExperTAX: an expert system for corporate taxplanning', Expert Systems, July, 136-51.

Waterman, D.A. (1986) A Guide to Expert Systems,Reading, MA: Addison-Wesley.

West (1988) West's Law Finder, St Paul, MN: WestPublishing.

121