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<Paper uid="C92-3147">
  <Title>B-SURE: A BELIEVED SITUATION AND UNCERTAIN-ACTION REPRESENTATION ENVIRONMENT</Title>
  <Section position="6" start_page="0" end_page="0" type="concl">
    <SectionTitle>
6. Probability Estimation
</SectionTitle>
    <Paragraph position="0"> The probability of an outcome situation i occurring following performance of an uncertain action is estimated using the new estimator ~ instead of ~, where m is the total number of previously observed trials of that action type, k~ is the previously observed number of ith situation-type outcomes, and n is the number of known possible outcome situations from that action. The new estimator is optimal. It represents the center of mass of all possible probabilities, instead of the maximum-likelihood mode; it converges faster and on average is more accurate than the old estimator; and, it can be used accurately with small sample numbers and small snccess counts \[Mye92\].</Paragraph>
    <Paragraph position="1"> 7.Maintaining an Interactive History One important advantage of the B-SURE system is that not only can it be used for hypothetical reasoning about future events, but the same structures can then be used as a history mechanism for interastively monitoring and representing the history of the actual events as they occur. A user system should start out in a known situation, which is presumed actual.</Paragraph>
    <Paragraph position="2"> Typically, the user system will use B-SUrtE to explore many different nondeteeministic-action sequences and make decisions ~s to which actions are tile best ones to perform. The system will then start executing the first action m tile chosen sequence. At. this point, tile user system should instruct tile B-SUItE system to presume the chooses assumption associated with tile chosen action being executed, which will change its truth value from &amp;quot;possibly believed true&amp;quot; to &amp;quot;delinitely believed true&amp;quot;. If the chooses node has already been made inconsistent with other chooses nodes (because the user-system or agent could ouly perform one action at a time), those other nodes are automatically rendered &amp;quot;believed not-true&amp;quot; at this point. The presumption of the chooses node renders the associated implied Action Event instantiation &amp;quot;definitely believed true&amp;quot; at this point, ,also. This represents the fact that the action has stated and is currently being execnted.</Paragraph>
    <Paragraph position="3"> When the action finishes, it is necessary for the B-SURE system to realize which outcome occurred.</Paragraph>
    <Paragraph position="4"> This is typically performed by the system setting up a recognition demon that is attached to a separate state or situation type that, when true, reliably indicates that a given outcome has occurred. When the demon fires, it presumes the outeome's happens assumption.</Paragraph>
    <Paragraph position="5"> It is important to ensure ttlat one and only one recognition demon fires. Alternatively, tile user can control presuming the happens nodes directly. When a single happens assumption is presumed, it automatically renders its sibling happens assumptions &amp;quot;de~init ely believed not;-tzale&amp;quot;.</Paragraph>
    <Paragraph position="6"> The combination of tile happens node being presumed and the action event node already being believed true renders the appropriate resulting situation instance believed true. Note that if any instance becomes true, so does its associated type node as well.</Paragraph>
    <Paragraph position="7"> At any one point in tinm, the states, situations, and action event instances that have happened in the world already are believed true; and the situations and events that have not happened yet but could happen are believed possible. In this way, the system maintains a timehne history of the situations and action events that have in fact occurred, while allowing hypothetical planning and exploration of possible future events in the same data structure.</Paragraph>
    <Paragraph position="8"> It is not necessary for the system to maintain only a single timeline history. It is possible to maintain disjoint histories, to represent e.g. progress made by different processing agents, progress made in different domains, or progress made at different hierarchical levels of abstraction. It is possible to maintain forking (nondisjoint) histories if this makes sense, and tim mutual exclusion options have been turned off (see Section 10).</Paragraph>
    <Paragraph position="9"> Counterfaetuals The system maintains the structures of past possibilities that did not happen.</Paragraph>
    <Paragraph position="10"> Although these are not believed true, it is possible for the user to explore these structures and perform reasoning on what could have occurred had certain actions been chosen or certain nondeterministic outcomes happened, by supplying an extra counierfactual assumption to justify the desired action or sit- null uation instance. It is even possible to add to these structures, if necessary. This can be used to explicitly represent newly-received p~st connterfactual iuformation (e.g., &amp;quot;If you had applied for the conference last June, the cost would have been 35,000 yen&amp;quot;) and the associated reasoning derived from snch assertions.</Paragraph>
    <Paragraph position="11"> Such reasoning has traditionally been very difficult to represent, because of the negative truth values.</Paragraph>
    <Paragraph position="12"> 8. Decision Inference Example A researcher is calling a conference office from tbe tram station and wants to get to the conference on time. He has a choice between asking for taxi directions, or requesting the office to send a shuttle-bus out directly to give him a ride. The shuttle will take him directly to the conference on time. If he requests and the office turns him down, he has a choice between taking a taxi, and taking the regular bus.</Paragraph>
    <Paragraph position="13"> These cost ditferent amounts of money and have different chances of getting to the conference on time.</Paragraph>
    <Paragraph position="14"> See figure 3. The plan inference system must predict which paths of information he will explore, i.e.</Paragraph>
    <Paragraph position="15"> what he will say next; and then which decisions he will make for his actions. This is done using &amp;quot;decision infcrence&amp;quot;, by understanding which action trees offer the best expected value based on the value and chances of outcomes. Note that the shuttle-bus, the taxi, and the regular bus will all three allow the researcher to possibly obtain his desired goal, but there are definite preferences. The system should not remain uncommitted. See \[Mye91\] for more details.</Paragraph>
    <Section position="1" start_page="0" end_page="0" type="sub_section">
      <SectionTitle>
9.Intentional Communication Example
</SectionTitle>
      <Paragraph position="0"> A recent analysis of 12 actual interpreted telephone conversations revealed that 31% of the utterances were spent in requests for confirmation and repetitions of information such as telephone numbers, name spellings, and addresses, that were not completely understood the first time \[OCP90\]. This means that the traditional plan-recognition model of assuming that the hearer automatically understands the semantic content of the speaker's utterance is fallacious. The speaker, and the system too, must consider the case in which the hearer does not understand an utterance. Since the speaker wants and intends to communicate specific information 2, the speaker will endeavor to ensure that the information is communicated, by repeating an utterance when it is not understood.</Paragraph>
      <Paragraph position="1"> Thus, speaking an utterance is a nondeterministie ac-Lion; it, is unclear whether the hearer will uuderstaald or not. Intentional utterauce acts are therefore modeled ~ nondeterministic-outcome actions by B-SURE.</Paragraph>
      <Paragraph position="2"> l)ifi'erent courses of the conversation cat\] be represented depending upon the outcomes of the utterance acts. See Figure 4.</Paragraph>
      <Paragraph position="3"> 10. Process Scheduling Example The application of the BEHOLDEIL a limited-resource parallel sehednling system to translation systems is being researched. A hypothetical model system is used for testing. The system will accept an input caa|didate from a speech recognition module, and attempt to quickly transfer tim result directly to output. If required, a morphologicd analyzer will derive multiple possible analyses candidates for each input candidate. A pattern marcher will then recursively apply a body of patterns to each analysis candidate.</Paragraph>
      <Paragraph position="4"> Each pattern has a series of transfer-driven translation templates; each template has a series of prototypicai exmnple bindings. The highest-ranking structure of matching nested patterns and their bindings are sent to a template marcher. The distances between the pattern bindings and the template examples for each pattern in the structure are compared using a thesaurus. The template with the closest match for each pattern will be used to assemble a translation.</Paragraph>
      <Paragraph position="5"> It is the responsibility of the BEHOLDER system to schedule this activity in an opportunistic fashion on multiple processors. There is no need to continue to explore a branch if a good translation has been found.</Paragraph>
      <Paragraph position="6"> The BEH OLDER system must use value-of-information theory and decision theory to determine which process branches to explore next and when to stop.</Paragraph>
      <Paragraph position="7"> The BEIIOLDER scheduler uses the B-SURE system to keep track of which processes are running and which have been executed. Using this representation, it can plan ahead and decide how useful it is to expand a particular path of execution. As processes are started, the chooses nodes are presumed. Figure 5 shows a simulated run where the direct transfer, the morphological analysis, one pattern match, and one template match have been run. The template match has examined two examples so far.</Paragraph>
      <Paragraph position="8"> Since in this ease more than one a~tion can be executed at a time, and one action cau legally have more than one possible outcome, it was necessary to modify the n-SORE system to allow local disabling of the 2Note that people do not always decide to intend to endeavor to do everything that they weatt. Intending is qlfite different from wmlting.</Paragraph>
      <Paragraph position="9"> aBeneficlal Entity for Heuristically Ordering processes un- null tation tool is required for representing past, present, and future nonmonotonic actions, when the actions \[dK86\] can have nondcterministic outeonms. The B-SURE euviromnent offers such a tool. Being able to model realistic actions allows exploration of significant prob- \[MN86\] |eros in situation modeling, plan inference, intentional actions research, and value-of-information theory 0.~ applied to parallel process scheduling. \[Mye89\]</Paragraph>
    </Section>
  </Section>
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