| { |
| "paper_id": "P81-1012", |
| "header": { |
| "generated_with": "S2ORC 1.0.0", |
| "date_generated": "2023-01-19T09:15:12.452339Z" |
| }, |
| "title": "TWO DISCOURSE GENERATORS", |
| "authors": [ |
| { |
| "first": "William", |
| "middle": [ |
| "C" |
| ], |
| "last": "Mann", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "USC Information Sciences Institute", |
| "location": {} |
| }, |
| "email": "" |
| } |
| ], |
| "year": "", |
| "venue": null, |
| "identifiers": {}, |
| "abstract": "", |
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| "paper_id": "P81-1012", |
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| "abstract": [], |
| "body_text": [ |
| { |
| "text": "The task of discourse generation is to produce multisentential text in natural language which (when heard or read) produces effects (informing, motivating, etc.) and impressions (conciseness, correctness, ease of reading, etc.) which are appropriate to a need or goal held by the creator of the text.", |
| "cite_spans": [ |
| { |
| "start": 132, |
| "end": 161, |
| "text": "(informing, motivating, etc.)", |
| "ref_id": null |
| } |
| ], |
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| "sec_num": null |
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| "text": "Because even little children can produce multieententiaJ text, the task of discourse generation appears deceptively easy. It is actually extremely complex, in part because it usually involves many different kinds of knowledge. The skilled writer must know the subiect matter, the beliefs of the reader and his own reasons for writing. He must also know the syntax, semantics, inferential patterns, text structures and words of the language. It would be complex enough if these were all independent bodies of knowledge, independently employed. Unfortunately, they are all interdependent in intricate ways. The use of each must be coordinated with all of the others.", |
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| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "For Artificial Intelligence, discourse generation is an unsolved problem.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "There have been only token efforts to date, and no one has addressed the whole problem. Still, those efforts reveal the nature of the task, what makes it diffic;,It and how the complexities can be controlled.", |
| "cite_spans": [], |
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| "section": "", |
| "sec_num": null |
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| "text": "In comparing two AI discourse generators here we can do no more than suggest opportunities and attractive options for future exploration. Hopefully we can convey the benefits of hindsight without too much detailed description of the individual systems. We describe them only in terms of a few of the techniques which they employ, partly because these tschnk:lUes seem more vaJuable than the system designs in which they happen to have been used.", |
| "cite_spans": [], |
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| "text": "The systems which we study here are PROTEUS, by Anthony Davey at Edinburgh [Davey 79] , and KDS by Mann and Moore at ISI [Mann and Moore 801. As we will see, each is severely limited and idiosyncratic in scope and technique. Comparison of their individual skills reveals some technical opportunities.", |
| "cite_spans": [ |
| { |
| "start": 48, |
| "end": 64, |
| "text": "Anthony Davey at", |
| "ref_id": null |
| }, |
| { |
| "start": 75, |
| "end": 85, |
| "text": "[Davey 79]", |
| "ref_id": null |
| }, |
| { |
| "start": 121, |
| "end": 130, |
| "text": "[Mann and", |
| "ref_id": null |
| } |
| ], |
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| "section": "THE TWO SYSTEMS", |
| "sec_num": null |
| }, |
| { |
| "text": "Why do we study these systems rather then others? Both of them represent recent developments, in Davey's case, recently published. Neither of them has the appearance of following a hand-drawn map or some' other humanly-produced sequential presentation. Thus their performance represents capabilities of the programs more than cs4)abilities of the programmer. Also, they are relatively unfamiliar to the AI audience. Perhaps most importantly, they have written some of the best machine-produced discourse of the existing art.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "THE TWO SYSTEMS", |
| "sec_num": null |
| }, |
| { |
| "text": "Rrst we identify particular techniclues in each system which contribute strongly to the quality of the resulting text. Then we compare the two Systems discussing their common failings and the possibilities for creating a system having the best of both.", |
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| "ref_spans": [], |
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| "section": "THE TWO SYSTEMS", |
| "sec_num": null |
| }, |
| { |
| "text": "PROTEUS creates commentary on games of tic.tac-toe (noughts and crosses.) Despite the apparent simplicity of this task, the possibilities of producing text are rich and diverse. (See the example in Appendix .)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "DAVEY'S PROTEUS", |
| "sec_num": null |
| }, |
| { |
| "text": "The commentary is intended both to convey the game (except for insignificant variations of rotation and reflection), and also to convey For example:", |
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| "eq_spans": [], |
| "section": "DAVEY'S PROTEUS", |
| "sec_num": null |
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| "text": "\u2022 Best move VS. Actual move: The move generators are used to compute the \"best\" move, which is compared to the actual one. If the move generator for the best move has higher rank than any generator proposing the actual move, then the actual move is treated as s mistake, putting the best move and the actual move in contrast.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "DAVEY'S PROTEUS", |
| "sec_num": null |
| }, |
| { |
| "text": ".Threat VS. Block: A threat contrasts with an immediately following block. This contrast is a fixedreflex of the system. It seems accedteble to mark any goal pursuit followed by blocking of the goaJ as contrastive.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "DAVEY'S PROTEUS", |
| "sec_num": null |
| }, |
| { |
| "text": "Sentence scope is determined by several heuristic rules including I. Express as many contrasts as possible explicitly. (This leeds to immediate selection of words such as \"but\" and \"however\".)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "DAVEY'S PROTEUS", |
| "sec_num": null |
| }, |
| { |
| "text": "2. Limit sentences to ,3 clauses.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "DAVEY'S PROTEUS", |
| "sec_num": null |
| }, |
| { |
| "text": "3. Put as many clauses in a sentence as possible.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "DAVEY'S PROTEUS", |
| "sec_num": null |
| }, |
| { |
| "text": "4. Expmas only the worst of several mistakes.", |
| "cite_spans": [], |
| "ref_spans": [], |
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| "section": "DAVEY'S PROTEUS", |
| "sec_num": null |
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| "text": "The main clause struotum is built before entering the grammar, Both the move characterization process and the use of contrasts as the principal ~ of sentence scope contribute a great deal to the quality of the resuRing text. However, Davey's central concern was not with these two 9rocessos but with the third one, sentence generation. 3. Unity. Since the grammar is defined in a single pubilcalion with a single 8uthomhiD, the is*ups of compatibility Of parts are minimized,", |
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| "ref_spans": [], |
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| "section": "DAVEY'S PROTEUS", |
| "sec_num": null |
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| "text": "It is intemsUng that Oevey does not employ the Systemk: Grlmm~lr dehvstJon rules at the highest level Although the grammer is defined in terms of the generation of sentences, Devoy entem it at the clause level with 8 sents~cs desc~Dtlon whi\u00a2;h conforms to Systemic Grammar but was built by other means. A sentence st this level is temporal principally of Ctl-_,,~__. but the surface conjunotlens have already been chosen.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "DAVEY'S PROTEUS", |
| "sec_num": null |
| }, |
| { |
| "text": "Although Oavey real(as no claim, this may redrasent a gener~d result about text generation systems. Above some level of al:atnm~on in the text planning proces~ planning is not conditioned by the content of the grammar.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "DAVEY'S PROTEUS", |
| "sec_num": null |
| }, |
| { |
| "text": "The obvious place to exbeot planning tO become indegendertt of the grammar is at the sentence I~.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "DAVEY'S PROTEUS", |
| "sec_num": null |
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| "text": "But in both PROTEUS and KD.~ Operations independent of the grammar extend down to the level of independent clm within sentences. Top leve~ coniunctlons am not within such ci~,~__; so they are determined by Dlenning pr~ before the grammar is enter~l.", |
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| "section": "DAVEY'S PROTEUS", |
| "sec_num": null |
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| "text": "It would be extremely awkward to implement Oavey'$ sentence s\u00a2obe heuristics in a syetamic grammar. The formalism is not well suited for oDer~tion~ such as maximizing the total number of explicit contrastive (dements. However, the problem is not just a i~rololem with the formalism; grammars generally do not deal with this sort of operations, and so are ~oorly equil~ped to do so. them i~ no need to \"rw~nm\" it. G~mo~ ~ dlvid~d imo ~ Id~ ~ &\u00a2~Iv~.", |
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| "section": "DAVEY'S PROTEUS", |
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| "text": "mle-ll3t~lca~nL A sylmlm of choi\u00a2.e~ (surJ1 u t~e ch~\u00a2~ i~lt~men -d~ -ind \"~mm,e\" kT,~,-,~ de.minim *,vh~J~ cm \"ai~-ttve') is mech~ ~ other cboP~a ~d w is \u00a2ondi~m\u00a2. but cny ch~\u00a2e. ~\u00a2e mechU, \".. ,.m,~m~rair,~L . Ru~ S~lUenc~ ~ femunl-emL ejcn ~ tl~ \"We~l.\" ~ml\u00a2~ enet~le ieed\u00a2~ m,l~\u00a2ltu~ enl n~ tm'efenemmnL Although the computer scientist who tries to learn from [Oavey 79} will find that it presents difficulties, the underlying system is interesting enough to be worth the trouble. Devey's imDiementation generally allam~s to be orthodox, conforming to [Hudson 71 ]. Davey regularizes some of the rules toward type uniformity, and thus reduces the apparent correspondence to Hudson's formulabons. However, the linguistic babe does not appear to have been compromised by the implementation.", |
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| "start": 184, |
| "end": 215, |
| "text": "mechU, \".. ,.m,~m~rair,~L . Ru~", |
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| "start": 556, |
| "end": 566, |
| "text": "[Hudson 71", |
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| "section": "DAVEY'S PROTEUS", |
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| "text": "One of the major strengths of the work is that it takes advantage of s comprehenal~, explicit and linguistically justified grammar.", |
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| "section": "DAVEY'S PROTEUS", |
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| "text": "Text quality is also enhanced by some simple filtering (of what will be expressed) based on demmdencies between known facts. Some facts dominate otherJ in the choice of what tO Say. If them is only one move on the board having a certain significance, say \"threat\", then the move is described by its significance alone, e.g. \"you threatened me\" without location informatic, n, since the reader can infer the locations. Similarly, only the most significant defensive and offensive aspects of a move ate described even though all are known.", |
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| "section": "DAVEY'S PROTEUS", |
| "sec_num": null |
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| "text": "The resulting text is divn) and of good quality. Although them ere awlo~mrdn __es,~__~ the immense advantage conferred by using a sophisticated grammar prevails.", |
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| "text": "SOace precJudes a thocou0h description of KDS, but fuller deecriptione are mml~ie [Mann and Moore 80] , [Mann 79] , [Moore 7% KDS consists Of five me~r modules, as indicated in Figure 2 . A Frl~lmentM is re~oonalble for eXtnL~ing the relevant knowledge from the notation given to it and dividing that knowledge into small exl:nmalble units, which we call fragments or pmtosentance\u00a2 A Prod=~m Solver, a goal-Oumuit engine in the AI tradition, is responsible for seeotlng the I~eUntmlm~d style of me text and ~ for iml~l~ng the grol8 ol~glmlze~Ion onto the text accordlng to m8~ style. A Knowk~ge Rater removes protasentencas that need not be expressed because they would be redundant to the medsr. The I~est and moat interesting r~__,_,~e is the Hill Climber, which has three raspon~billtis\u00a2 tO compose complex i:rotoasntences from simpM one~ tO judge relative quality among the units resulting from compo~dtton, and to repeatedly improve the set of protosentencas on the Ioasm of those judgments so thM it is of the highest eyeful quality. Finally. s very simple Surface Sentence Maker cremes the sentences of me final text out of protoaec~lmc~.", |
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| "start": 82, |
| "end": 101, |
| "text": "[Mann and Moore 80]", |
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| "start": 104, |
| "end": 113, |
| "text": "[Mann 79]", |
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| "start": 116, |
| "end": 125, |
| "text": "[Moore 7%", |
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| ], |
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| "start": 177, |
| "end": 185, |
| "text": "Figure 2", |
| "ref_id": "FIGREF1" |
| } |
| ], |
| "eq_spans": [], |
| "section": "MANN AND MOORE'S KDS Major Modules of KDS", |
| "sec_num": null |
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| "text": "The data flow of these modules can be thought of as a simple pipeline, each module processing the relevant knowledge in turn.", |
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| "section": "MANN AND MOORE'S KDS Major Modules of KDS", |
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| "text": "The principal contributors to the quality of the output text are:", |
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| "section": "MANN AND MOORE'S KDS Major Modules of KDS", |
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| "text": "1. The Fragment and Compose Paradigm: The information which will be expressed is first broken down into an unorganized collection of subsententiai (\u00a2oproximstely clause-level) propositional fragments. Each fragment is crested by methods which guarantee that it is expressible by a sentence (usually a very short one, This makes it possible to organize the remainder of the processing so that the text production problen~ is treated as an improvement problem rather than as a search for feasible solutions, a significant advantage.) The fragments are then organized and combined in the remaining processing.", |
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| "section": "MANN AND MOORE'S KDS Major Modules of KDS", |
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| "text": "2. Aggregation Rules: Clause-combining patterns of English are represented in a distinct set of rules. The rules specify transactions on the set of propositional fragments and previous aggregation results. In each transection several fragments are extracted and an aggregate structure (capable of representation as a sentence) is inserted. A representative rule, named \"Common Cause,\" shows how to combine the facts for \"Whenever C then X\" and \"Whenever C then Y\" into \"Whenever C then X and Y\" at s propositional level.", |
| "cite_spans": [], |
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| "section": "MANN AND MOORE'S KDS Major Modules of KDS", |
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| "text": "3. Preference Assessment: Every propositional fragment or aggregate is scored using a set of scoring rules. The score represents s measure of sentence quality. The knowledge domain of KDS' largest example is a Fire Crisis domain, the knowledge of what happens when there is a fire in a computer room. The task was to cause the reader, a computer operator, to know what to do in all contingencies of fire.", |
| "cite_spans": [], |
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| "section": "MANN AND MOORE'S KDS Major Modules of KDS", |
| "sec_num": null |
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| { |
| "text": "The most striking impression in comparing the two systems is that they have very little in common. In particular,", |
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| "section": "SYSTEI~ 1 (~OMPARISONS", |
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| "text": "1. KDS has sentence scoring and a quslity.based selection of I~ow to say things; PROTEUS has no counterp;u't.", |
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| "text": "2. PROTEUS has a sophisticated grammar for which KOS has only a rudimentary counterpart, 3. PROTEUS has only a dynamic, redundancy-based P, nowledge filtering, whereas the filtering in KOS removes principally St=~tic, foreknown information.", |
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| "text": "4. KDS has clause-combining rules which make little use of conjunctions, whereas PROTEUS has no such rules but makes elaborate use of coniunctions.", |
| "cite_spans": [], |
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| "section": "SYSTEI~ 1 (~OMPARISONS", |
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| "text": "5. KOS selects for brevity above all, whereas PROTEUS selects for contrast =hove all.", |
| "cite_spans": [], |
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| "eq_spans": [], |
| "section": "SYSTEI~ 1 (~OMPARISONS", |
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| "text": "6. PROTEUS takes great advantage of fact significance assessment, which KDS does not use.", |
| "cite_spans": [], |
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| "eq_spans": [], |
| "section": "SYSTEI~ 1 (~OMPARISONS", |
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| "text": "They have little in common technically, yet both produce high quality text relative to predecessors. This raises an obvious question--Could the techniques of the two systems be combined in an even more effective system?", |
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| "text": "There is one prominent exception to this general lack of shared functions and characteristics, Recent text synthesis systems [Davey 79] , [Mann end Moore 80] , [Weiner 80] , [Swartout 77 ], [Swartoutthesis 81], all include a facility for keeping certain facts or ideas from being expressed. There is an implicit or explicit model of the reader's knowledge. Any knowledge which is somehow seen as obvious to the reader is suppressed.", |
| "cite_spans": [ |
| { |
| "start": 125, |
| "end": 135, |
| "text": "[Davey 79]", |
| "ref_id": null |
| }, |
| { |
| "start": 138, |
| "end": 157, |
| "text": "[Mann end Moore 80]", |
| "ref_id": null |
| }, |
| { |
| "start": 160, |
| "end": 171, |
| "text": "[Weiner 80]", |
| "ref_id": null |
| }, |
| { |
| "start": 174, |
| "end": 186, |
| "text": "[Swartout 77", |
| "ref_id": null |
| } |
| ], |
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| "text": "All of the implemented facilities of this sort are rudimentary; many consist only of manually-ornduced lists or marks. However, it is clear that they cover a deep intellectual problem. Discourse generation must make differing uses of what the reader knows and what the reader does not know.", |
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| "text": "It is absolutely essential to avoid tedious statement of \"the obvious.\" Proper use of presupposition (which has not yet been attempted computationally) likewise depends on this knowledge, and many of the techniques for maintaining coherence depend on it as well. But identification of what is obvious to a reader is a difficult and mostly unexplored problem. Clearly, inference is deeply involved, but what is \"obvious\" does not match what is validly inferable. It appears that as computer-generated texts become larger the need for a robust model of the obvious will increase rapidly.", |
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| "text": "This section views the collection of techniques which have been discussed so far from the point of view of a designer of a future text synthesis system. What are the design constraints which affect the possibility of particular combinations of these techniques? What combinations are advantageous? Since each system represents a compatible collection of techniques, it is only necessary to examine compatibility of the techniques of one system within the framework of the other.", |
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| "section": "POSSIBILITIES FOR SYNTHESIS", |
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| "text": "We begin by examining the hypothetical introduction of the KDS techniques of fragmentation, the explicit reader model, aggregation, preference scoring and hill climbing into PROTEUS. We then examine the hypothetical introduction of PROTEUS' grammar, fact significance assessments and use of the contrast heuristic into KDS. Finally we consider use of each system on the other's knowledge domain.", |
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| "section": "POSSIBILITIES FOR SYNTHESIS", |
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| "text": "Introducing KDS teohniques into PROTEUS Fragment and Compose is clearly usable within PROTEUS, since the information on the sequence of moves, particular move locations and the significance of each move all can be regarded as composed of many incleDendent propositions (fragments of the whole structure.) However, Fragment and Compose appears to give only small benefits, principally because the linear sequences of tic-tac-toe game transcripts give an acceptable organization and do not preclude many interesting texts.", |
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| "section": "POSSIBILITIES FOR SYNTHESIS", |
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| { |
| "text": "Aggregation is also useable, and would appear to allow for a greater diverSity of sentence forms than Oavey's Secluential assembly torocedures allow.", |
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| "section": "POSSIBILITIES FOR SYNTHESIS", |
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| "text": "In KDS, and presumably in PROTEUS as well, aggregation rules can be used to make text brief, in effect, PROTEUS already has some aggregation, since the way its uses of conjunction shorten the text is similar to effects of aggregation rules in KDS.", |
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| "section": "POSSIBILITIES FOR SYNTHESIS", |
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| { |
| "text": "Prefei'ence judgment and Hill climbing are interQependent in KDS.", |
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| "ref_spans": [], |
| "eq_spans": [], |
| "section": "POSSIBILITIES FOR SYNTHESIS", |
| "sec_num": null |
| }, |
| { |
| "text": "Introducing both into PROTEUS would appear to give great improvement, especially in avoiding the long awkward referring phrases which PROTEUS i=roduced. The system could detect the excessively long constructs and give them lower scores, leading to choice of shorter sentences in those cases.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "POSSIBILITIES FOR SYNTHESIS", |
| "sec_num": null |
| }, |
| { |
| "text": "The Explicit Reader model could also be used directly in PROTEUS; it would not help much however, since relatively little foreknowledge is involved in any tic-tac-toe game commentary/.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "POSSIBILITIES FOR SYNTHESIS", |
| "sec_num": null |
| }, |
| { |
| "text": "Introducing PROTEUS techniques into KDS Systemic Grammar could be introduced into KDS to great advantage. The KDS grammar was deliberately chosen to be rudimentary in order to facilitate exploration above the sentence level. (In fact. KDS could not be extended in any interesting way without ulxJrading its grammar.) Even with a Systemic Grammar in KDS, aggregation rules would remain, functioning as sentence design elements.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "POSSIBILITIES FOR SYNTHESIS", |
| "sec_num": null |
| }, |
| { |
| "text": "Fact significance assessments are also compatible with the KDS design. As in PROTEUS they would immediately follow aoduialtion of the basic grogositianeL They could improve the text significantly.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "POSSIBILITIES FOR SYNTHESIS", |
| "sec_num": null |
| }, |
| { |
| "text": "The contrast heuristic (and other PROTEUS heuristics) would fit well into KDS, not as an a priori sentence design device but as a basis for assigning preference. Higher score for contrast would improve the text.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "POSSIBILITIES FOR SYNTHESIS", |
| "sec_num": null |
| }, |
| { |
| "text": "In summary, the principal techniques appear to be completely compatible, and the combination would surely produce better text than either system alone.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "POSSIBILITIES FOR SYNTHESIS", |
| "sec_num": null |
| }, |
| { |
| "text": "The tic-tac-toe domain would fit early into KDS` but the KOS text-organization Drocesles (not discuased in this I:~ger) would have littJe to do. The fire crisis domain would be too complex for PROTEUS. It involves several actorS at once, several parallel contingencies and no single clear organizing principle. PROTEUS lacks the necessary text-organization methods.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Exchange of Knowledge Domains", |
| "sec_num": null |
| }, |
| { |
| "text": "These systems share (with many others) the i=rimitive state of the computer.be,sad discourse-generation a~. Their groce~,~l are [=rimarily devoted to activities that go without notice among literate I~eogle. The deeper linguistic and metorical phenomena usually associated with the term \"discourse\" are hardly touched. These systems make little attempt at coherence, and they do not respond in amy way to the coherence (or lack of it) which they achieve. Presupposition, topic, focus, theme, the pro~er role of inference, imglicature, direct and indirect Sl:~ech act performance and a host of other relevant concepts all go unrepresented. Even wome, the 46 underlying conceotual agpars.tus in both systems is extremely adhoc and idiosyncratic, severely limiting the I=OSSiblities for using general knowledge of the semantics of English.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "SHARED SHORTCOMINGS", |
| "sec_num": null |
| }, |
| { |
| "text": "Despite these deficiencies, the systems produce relatively smooth readable texL They are significant principally as collections of methods which may well survive to become l=arts of more competent and general.purOose systems of the future.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "SHARED SHORTCOMINGS", |
| "sec_num": null |
| }, |
| { |
| "text": "PROTEUS and KDS are distinct collections of discourse generation techniques. There is little overlap between them, and no a~patent conflict involved in using the techniques in combination. A system which combined the strengths of both would be significantly better than either.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "CONCLUSIONS", |
| "sec_num": null |
| }, |
| { |
| "text": "APPENDIX: TEXT SAMPLES FROM THE T~NO SYSTEMS KDS: Fire-Alarm Text for Computer Operstors Whenever there is a fire, the alarm system is started, which soul!de a bell and starts a timer. Ninety seconds after \u2022 e timer startlk unless the alarm system is cancelled, the system calls Wells Fargo. When Wells Fargo is called, they, in turn, call the Fire Department.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "CONCLUSIONS", |
| "sec_num": null |
| }, |
| { |
| "text": "When you hear the alarm bell or smell smoke, stop whatever you are doing, determine whether or not there is a \u2022 fire, and decide whether to permit the ala~;m system or to cancel it. When you determine whether there is a fire, if there is, permit the alarm system, othewvise cancel iL When you i~\u00a2mit the alarm system, call the Fire Department if possible, then evacuate. When you cancel the alarm system, if it is more then 90 seconds since the timer started, the system will have called Wells Fargo already, cth~ continue what you were doing. The game started with my taking a comer, and you took an adjacent one. I threatened you by taking the middle of the edge.opposite that and adjacent to the one which 1 had just taken but you blocked it and threatened me. I blocked your diagonal and forked you. If you had blocked mine, you would have forked me, but you took the middle of the edge oppoalte the corner which I took first and the one which you had just taken and so I won by completing my diagoned.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "CONCLUSIONS", |
| "sec_num": null |
| } |
| ], |
| "back_matter": [], |
| "bib_entries": { |
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| "FIGREF0": { |
| "num": null, |
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| "text": "the significance of each move, including showing errors and missed opportunities. PROTEUS can be construed as consisting of three 13rincipal processors, as shown in Figure 1. Move characterization employs a ranked set of move generators, each identified as defensive or offensive, and each identified further with a named tactic such as blocking, forking or completing a win. A move is characterized as being a use of the tactic which is associated with the highest-ranked move generator which can generate that move in the present situation\u2022 The purpose of move characterizaiton is to intefl:ret the facts so that they become significant to the reader. (Implicitly, the system embodies a theory of the significance Principal Processors of PROTEUSContrastarises between certain time-adiacent moves and also between an actual move and alternative possibilities at the same point.", |
| "type_str": "figure" |
| }, |
| "FIGREF1": { |
| "num": null, |
| "uris": null, |
| "text": "KDS Module Resgonsibilltiss", |
| "type_str": "figure" |
| }, |
| "FIGREF2": { |
| "num": null, |
| "uris": null, |
| "text": "Hill Ctimbing: Aggregation and Preference Assessment are aJternated under the control of a hill-climbing algorithm which seek.'s to maximize the overall quality of the collection, i.e. of the complete text. This allows a clean separation of the knowledge of what could be said from the choice of whet should be said. 5. Knowledge Filtering: Propositions identified by an extolicit model of the Reader's knowledge as known to the reader are not exl:resasd.", |
| "type_str": "figure" |
| } |
| } |
| } |
| } |
