A computational model of misunderstandings in ? agent communication Liliana Ardissono, Guido Boella and Rossana Damiano Dipartimento di Informatica - Universita di Torino Corso Svizzera n.185 - 10149 Torino - Italy E-mail: fliliana,
[email protected] Fax: +39-11-751603; Phone: +39-11-7429111
Abstract. In this paper, we describe a plan-based model for the treat-
ment of misunderstandings in NL cooperative dialogue: an utterance is considered coherent if it is related to some of the interactants' intentions by a relation of goal adherence, goal adoption or plan continuation. If none of them is fully satis ed, a misunderstanding is hypothesized and the goal of realigning the interactants' interpretation of the dialogue is adopted by the agent detecting the problem. This goal makes the agent restructure his own contextual interpretation, or induce his partner to do that, according to who made the mistake.
1 Introduction We describe a plan-based model of misunderstandings within an agent architecture supporting cooperative NL communication. In a dialogue, a misunderstanding occurs when an interactant chooses an interpretation for some turn which is complete and coherent from his point of view, but it is not the one intended by the speaker [21]. A wrong interpretation of one or more turns can cause a misalignment of the speakers' dialogue contexts, with the eventual production of a turn perceived as incoherent by its hearer. At this point, however, either the speaker or the hearer might have constructed separately an erroneous dialogue context, so it is necessary to exploit the incoherent turn to identify the responsible for the mistake. We model the treatment of misunderstandings as a goal-directed, rational behavior: our notion of coherence in communication is based on the idea that the goals identi ed when interpreting a turn have to be related with the previously expressed or inferred goals of the interactants [2, 8]. When an agent receives an incoherent turn (or a turn which only partially matches his expectations), he adopts the further goal of realigning the interactants' diverging interpretations. To do so, he reasons to understand which agent misunderstood the other one; then, he can restructure his own interpretation, or make the partner restructure ?
We are indebted to Prof. Leonardo Lesmo and Prof. Carla Bazzanella for their fruitful discussions and hints to our work. This work has been supported by MURST and CNR, project \Piani cazione e Riconoscimento di Piani nella Comunicazione".
his accordingly. Repair turns are explained as cooperative subdialogues aimed at restoring the common interpretation ground; in fact, the maintenance of a correct dialogue context is a mutual goal of the interactants [10]. Incoherent turns are not always due to misinterpretations: also topic shifts and breakdowns in cooperation should be considered. Currently, we don't model topic shifts due to the initiation of new dialogues; however, as pointed out by many researchers [11, 17, 18], focus and topic shifts are usually marked by the presence of \cue" words. On the contrary, since we model cooperative dialogues, we exclude the hypothesis that a breakdown in communication can occur.
2 Background Scheglo [24, 25] has analized misunderstandings with respect to the sequences of turns of a dialogue, in order to identify the mechanisms used by the interactants to defend the \intersubjectivity", i.e. the common set of beliefs necessary for an interaction to continue successfully. He points out that speakers monitor their partners' reactions and interpret them as displays of understanding / misunderstanding of the previous turns. [25] identi es di erent repairs to misunderstandings, according to their position in the dialogue and whether the agent performing them is correcting himself, or his partner. In particular, in \third position repair", the misunderstood agent realizes that the partner has a wrong interpretation and urges him to restructure it: Example1 (translated from [14]): T0: B: \Hello. I'd like to have an English book [...] for the American school. Is there a 10% discount?" T1: A: \Do you have the enrollment card?" T2: B: \Yes, I have it." T3: A: \No, you should show me your card." T4: B: \Oh I understand." Independently from the actual turn position, this corresponds to a repair accomplished by the misunderstood speaker in one of his own subsequent turns, i.e. a \self-repair". Instead, in \fourth position repair", a speaker A realizes that he has misunderstood a turn T 1 uttered by his partner only after having replied to it (T 2) and having received back a turn T 3 incoherent with respect to T 1 and T 2 (T 3 is coherent with the partner's intended interpretation of T 1). Typically, after having corrected his interpretation, A performs another type of repair, telling the partner that he has understood what he initially meant: Example2 (from [25]): T1: Marty: \Loes, do you have a calendar," T2: Loes: \Yeah" ((reaches for her desk calendar)) T3: Marty: \Do you have one that hangs on the wall?" T4: Loes: \Oh, you want one."
T5: Marty: \Yeah" Since these repairs are typically accomplished by the interlocutor who has misunderstood in one of his turns, these turns are called \other-repairs", referring to the agent they belong to.
3 Our agent model The work presented in this paper extends our agent model to enable it to cope with misunderstandings in NL communication. Our agent architecture has a two-level plan-based representation of the knowledge about acting [4, 5]. At the metalevel, the Agent Modeling (AM) plan library describes the (precompiled) problem-solving recipes used by an agent to choose the actions for reaching his goals, and to execute them; at the object level, the Domain library describes recipes for obtaining domain goals (such as buying a book, see Example1), and the Speech Act Library describes the direct and indirect speech-acts [3]. The three libraries are composed of a Generalization Hierarchy and a Decomposition Hierarchy [16] and share the same representation formalism. The AM actions take domain actions and speech-acts as objects: an agent performing a problem-solving activity can plan to perform domain and linguistic actions. In the AM actions, one parameter (source) denotes the bene ciary of the action that the modeled agent is performing: usually, source is bound to the agent of the AM action itself; however, it can be used to model cooperation between agents. There are two major actions in the AM library: { \Satisfy(agt, source, g )" describes the behavior of an agent agt, who wants
to satisfy a goal g; its body consists of looking for a feasible plan for g and executing it. If no feasible plan is found, the decomposition of \Satisfy" includes the noti cation to the source agent that the goal cannot be reached (see the notion of Joint Intention in agent cooperation [10]). { \Try-execute(agt, source, action)" describes the execution of actions; it includes checking the preconditions of action and performing it (possibly expanding its decomposition, if it is complex). The decomposition of \Tryexecute" also describes acknowledgements in agent cooperation: after a successful execution of action, if agt is collaborating with another source agent, he has to inform him that the action has succeeded; on the contrary, if the action could not be executed, he has to inform him of the problem.
The agent's behavior is ruled by an interpreter which loops on three phases, each one performed by means of a call to a \Satisfy" action: interpretation of the input (P1), decision of which goal to commit to (P2), and reaction (P3, where the agent acts to reach the chosen goal). The plan-based representation of knowledge supports a declarative representation style, that can be used both for interpreting and generating the agent behavior. A basic assumption of the recognition task is that all agents have equal plan libraries (on the contrary, we don't assume that agents have equal beliefs about the world state).
The agent interprets a dialogue incrementally: for each turn, he builds the semantic representation of the input sentence [6], nds the goal which the sentence is aiming at, and relates it with the dialogue context. The interpretation of each turn consists of a Context Model (CM), which contains the problem-solving actions (AM) referring to the linguistic (SAM) and domain-level (DM) actions observed (or performed) by the agent: the AM actions explain the reasons for performing the object-level actions. The local CM built during the interpretation of each turn is embedded into the dialogue context by identifying how the sentence contributes to the previous interaction: the context contains the sequence of interpretations (CMs) of the turns (properly interleaved), together with the relations existing among them, as explained in the following section.
4 Coherence and misunderstandings We consider a new contribution coherent if a relation exists among the intentions underlying it and the previous pending intentions of the interactants, either expressed explicitly, or inferred by reasoning on their plans [9]. An utterance is coherent with the previous context if its receiver A can interpret it as a means of the speaker B to achieve a goal g such that: 1. Goal adherence: g is one of the goals previously expressed by A. 2. Goal adoption: g is one of the goals that B has inferred A is going to aim at; e.g. in: T1: A: \I need a book. Where is the library?" T2: B: \It is over there, but it is closed." B provides A with an extra-helpful information which satis es his next goal of checking whether the library is open [2]. 3. Plan continuation: g contributes to a plan that B is carrying on.2 E.g: T1 B: \Where is the library?" T2 A: \It's over there." T3 B: \Is it open today?" Goal adherence and adoption refer to a complete satisfaction of the hearer's pending intentions. So, when the partner satis es only some of the goals that the hearer expects him to obtain, although a partial match exists, the analyzed turn is not considered coherent with the previous part of the interaction.3 2
We assume that an agent continues the execution of his plans only when there aren't other goals of the partner to be achieved. 3 Actually, it is not necessary to satisfy all the pending goals separately. In a CM, some goals strictly depend upon other ones, at a higher level. In that case, the satisfaction of the latter makes the former irrelevant, so that they can be considered as \closed". For example, if somebody asks a question, he expects that his partner acknowledges in some way the fact that he has understood the utterance. However, if the partner answers the question directly, this satis es all the pending expectations and no other acknowledgement is required in order to be coherent.
Uncooperative replies (e.g. refusals) and subdialogues for solving interpretation problems are other types of coherent contributions. Although in this paper we do not take these phenomena into account, they have to be explained as related with some speaker's goal as well: respectively, the goal to know whether the hearer intends to cooperate, and that the hearer interprets the utterance. The interpretation phase (P1) is performed by means of an elementary \Buildinterpretation" action. Consider the basic interpretation of an input turn by its hearer A: \Do(B , Utterance-act(B , A, \input-utterance"))".4 As the result of a successful execution of \Build-interpretation", the agent A has a new dialogue context where the last turn is related with the previous context by one of the three relations described above. In principle, the previous context can be: - void (e.g. at the beginning of a dialogue); - composed of an elementary uninterpreted CM, simply containing the execution of an utterance act (\Do(agt, Utterance-act(...))"); - a complex context, composed of the interpretations of the previous turns, properly linked by coherence relations. The process of executing \Build-interpretation" is composed of a local and a global interpretation phase. In the description of these phases, we will represent the uninterpreted turns of the form \Do(agt, Utterance-act(...))" with the notation t . On the contrary, we will use the notation T to represent in a generic way either an uninterpreted turn or its local interpretation. i
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1. The local turn interpretation phase is called \upward-expansion", because it consists of an expansion of a CM along a path up in the plan libraries. In this phase, the possible (metalevel and object level) plan leading B to the execution of the turn is looked for, and the agent's current goals are identi ed. 2. In the global interpretation phase, the newly identi ed goals and their highlevel plans are related with the participants' goals pending from the previous dialogue context. This is done by means of the \matching" and \focusing" functions, which model two di erent relations among turns: { The \matching procedure" succeeds when the top-level goal, recognized in the local interpretation phase as the reason for B 's last turn T , corresponds to one of the (unsatis ed) goals ascribable to A. This goal has been expressed by A (see the notion of goal adherence above), or B can have inferred it during the interpretation of A's previous turns (goal adoption). { The \focusing procedure" succeeds when B 's turn T is the continuation of an AM plan performed by B (and previously identi ed by A).5 n
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In practice, these two phases are carried on together by a number of heuristic rules we have de ned. The rules exploit the speaker and hearer's contextual 4 5
In the CM, \input-utterance" is a string of characters. The continuation of an AM plan can correspond to the continuation of a domain or a communicative action [19].
Restructure
name: Restructure roles: ((agent x)(partner y)(turns [t1 , . . . ,tn ]) (old-inter ctx)(intended-inter ctxj )) var-types: ((person x y)(turn t1 . . . tn )(context ctx ctx ctxj )) e : inter(x, [t1 , . . . ,tj ], ctxj ) constr: inter(x, [t1 , . . . ,tn ], ctx) ^ inter(y, [t1 , . . . ,tn ], ctx ) ^ correct-subcontext(ctxj ,ctx,ctx ) ^ agent(y,ctxj [j]) 0
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Fig. 1. The \Restructure" action. pending goals to guide the interpretation of the new contribution toward these goals, by pruning the non promising directions. The rules look for the paths in the plan library relating an object-level action (underlying the input utterance) with the object-level plan built in the interpretation of the previous turns. Since a rule searches for the shortest path linking the last turn to the previous context, \Build-interpretation" provides at each call a di erent result, with a progressive degree of complexity. The hypothesis that a misunderstanding among the agents has occurred arises in the following situation: if, during the interpretation of an utterance, the \matching" and \focusing" procedures fail,6 no relation is established between the turn and the dialogue context. In this case, the interpretation phase returns a context where the last turn remains unrelated. In principle, two hypotheses are possible: a focus shift has occurred or the cooperation between the speakers has broken down. If neither hypothesis is feasible, the lack of coherence can be interpreted as a display that a misunderstanding has occurred. In this case, the agent (A) commits to the goal of realigning the interactants' subjective views of the dialogue; this is done by the execution of a \Satisfy" action on the goal that A's interpretation of the dialogue is the same as B 's: (i) Satisfy(A, B, (inter(A, [t1, . . . , tj ], ctxj ) ^ inter(B, [t1 , . . . , tj ], ctxj ))) The variables j and ctx occurring in the third argument of the \Satisfy" action are intended to be existentially quanti ed. The meaning of the complex formula containing them is that agents A and B have two equivalent interpretation contexts (ctx ) for the part of the dialogue from turn t1 to t . Although j and ctx are unbound when the \Satisfy" action is started by the agent, at the end of its execution they are bound respectively to the rst turn which was misunderstood and to the correct interpretation context of subdialogue [t1 , ..., t ]. The execution of (i) starts with a planning activity, where A identi es the action which has a change of dialogue interpretation in its e ects: \Restructure" (shown in Figure 1).7 j
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I.e. although the utterance is interpretable, at least, as a speech act, there is no interpretation of it such that the procedures above can nd a contextual pending goal to which the turn can be related. 7 The \Satisfy" action does not manage complex goals in a general way. It can however satisfy separately the subgoals of a conjunctive goal. In this particular formula, this is enough, because after the instantiation of \Restructure", variable ctxj is bound
Reinterpret(agt, [T1 , . . . , Tn ], ctx) begin i := n - 1; LC := ;; RestC := ;; while ( i > 0 ^ empty(LC)) do begin LC := Build-interpretation(agt, Ti , Tn , ctx); if empty(LC) then i := i - 1 else begin RestC := Reinterpret(agt, [T1 , . . . , LC[1]], ctx); if RestC then begin j := i + 1; while ( j < n ) do begin RestC := Build-interpretation(agt, RestC, Tj , ctx); j = j + 1; end; if empty(RestC) then LC := ; /*Try another local ctx*/ end else LC := ; /*Try another local context*/ end end; return(RestC) /* Return the reinterpreted context */ end. Fig. 2. The \Reinterpret" algorithm. In order to perform the \Restructure" action, its constraint must be true. The constraint of \Restructure" requires that: - The agent x (who has to perform the action to correct his wrong interpretation) has an interpretation ctx of the whole dialogue, up to the last turn t . - An alternative interpretation ctx of the dialogue can be attributed to x's partner y. - ctx is the coherent interpretation of the misunderstood speaker, up to the misinterpreted turn t (j < n). - The agent y corresponds to the speaker of the rst turn that has been interpreted di erently in ctx and ctx (i.e. the last turn of ctx , denoted as ctx [j] in Figure 1). In order to evaluate this constraints, the agent A must determine B 's interpretation context ctx , alternative to A's one (ctx). He does this by executing a \Satisfy(A, B , Knowref(A, ctx , inter(B , [t1 , . . . , t ], ctx ))". The \Knowref(...)" goal is obtained by executing an action corresponding to the \Reinterpret" algorithm described below. If, after the execution of \Reinterpret", the rst turn for which ctx and ctx di er has been uttered by A, then he has to persuade B to restructure his dialogue context, by means of a linguistic repair. Otherwise, A executes \Restrucn
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to the interpretation subcontext of one of the two speakers; so, it is not necessary to satisfy both the subgoals.
ture", changing his own interpretation to ctx (see the e ect of \Restructure"); after that, they can continue the interaction from the reestablished interpretation context. In both cases, the recovery goal is shared among the speakers; when everything ends up well, the agent informs the partner about the success of their aims (see turns T4 in Example 1 and 2); otherwise, if no reconstruction is feasible, the agent warns his partner that the intersubjectivity cannot be recovered (as the last step of \Satisfy" prescribes). We now describe the reinterpretation algorithm \Reinterpret", shown in Figure 2. The algorithm traces back the dialogue, turn after turn, and looks for a relation between the problematic turn T and some previous (misinterpreted) turn. \Reinterpret" has the following arguments: the agent (agt), a sequence of turns ([T1 , . . . , T ]) and the old context ctx (the agent's wrong interpretation of the dialogue). It tries to relate T to the alternative interpretations of a previous turn, in order to constrain the search for the correct interpretation of the dialogue and improve this search, with respect to a blind form of backtracking. It is worth noting that the agent executing the algorithm must consider the alternative interpretations of his own turns, beside those of the partner's turns. When \Reinterpret" is called on a list of uninterpreted turns [t1 , ..., t ], it goes backward from the problematic last turn t , towards the beginning of the dialogue. It stops when it nds the most recent turn t for which \Buildinterpretation" has found a new interpretation, coherent with t .8 Then, \Reinterpret" propagates the new interpretation LC (Local Context) to the whole context, using the interpretation T of t in LC (which corresponds to the rst component of this context, denoted by LC[1]) as a \pivot", backward by calling itself recursively, and forward by means of a loop of \Build-interpretation" on the remaining turns. j
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5 Example We now sketch how our model works on Example1; Figure 3 shows the intended interpretation of the clerk's (A) turns.9 In order to sell a book with a discount as requested by B , A must check whether B has the enrollment card (action \Satisfy(A, B, Knowif(have(B, Card)))" in AM1). Cautiously, he decides to obtain that goal by making B show him this card. He has to request B to show him the card, but he adopts an indirect strategy to suggest B that he wants to see her card: in T1, he asks her whether \she has" the card (a precondition of action \Show").10 8
Although \Build-interpretation" takes in input a context and a turn, there is no contradiction here, because a single turn is by itself an elementary context. 9 The gure represents the Agent Modeling plans and the object level actions, linked by means of arcs. 10 Although AM1 contains two equal occurrences of the \Satisfy" action, they do not lead to any loop. In fact, we constrain the search strategy in the AM library not to select any object-level action that the agent is already carrying on. In our example,
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Fig. 3. Intended interpretation of the clerk's (A) turns in Example1. In T2, B answers positively to the question; this is interpreted by A as a \Satisfy(B, A, done(B, Inform-if(B, A, have(B, Card))))", not shown in the gure. However, T2 alone is not a coherent reply to T1, because it leaves unsatis ed A's implicit expectation that B shows him the card. This expectation corresponds to the formula \SH(A, B, Cint(A,B, Goal(A, done(B, Show(B, A, Card)))))11 which is the e ect of A's request (in SAM1). So, A hypothesizes that a misunderstanding has occurred and commits to realigning the two interpretations, by performing action \Satisfy(A, B, inter(A, [t1 ,. . . ,t ], ctx ) ^ inter(B,[t1 ,. . . ,t ],ctx ))".12 A selects the \Restructure" action for obtaining his j
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the lower-level \Satisfy" for discovering the value of the constraint cannot be solved by another \Show" action. 11 In the formula, the SH modal operator represents mutual beliefs; the Cint modal operator, instead, represents communicative intentions, as de ned in [1]. Finally, Done maps an action on the (world) state where the action has been successfully executed. 12 In this case, the dialogue [t1 ,. . . ,tj ] reduces to T1, since j < n and n = 2: see AM2
goal, because the e ect \inter(x, [. . . ], ctx)" matches the goal to be obtained (it can be used to change interpretation context). As described in the AM library, before executing an action, its constraints must be evaluated.13 So, A performs a \Satisfy" action (not reported in the gure) to know if the constraints of \Restructure" are true. This leads A to nd an alternative interpretation context (CTX1 ') to be ascribed to B , by means of the \Reinterpret" algorithm: in fact, T1 is ambiguous since A's question could be interpreted as a direct way of learning from B 's answer whether she is enrolled. In this case, the ambiguity that misleads B is due to alternative reconstructions of the speaker's problem solving activity (in the gure, B 's wrong interpretation of T1 can be derived from AM1 by ignoring the contents of its inner dashed rectangle). In this second interpretation, T2 would be a coherent reply to T1, since it does not leave any unsatis ed goal. At this point, the misunderstood turn has been detected by A. Given that A is the misunderstood speaker, B should perform a \Restructure" action to correct her interpretation (to CTX1 ). So, A performs a \Get-to-do" action14 to induce her to do that: this leads A to plan a \Request" for \Restructure". The request would be expressed by making explicit his real intentions, which were implicit in T1.
6 Conclusions We have described a plan-based agent model for a computational treatment of misunderstandings in dialogue. In this model, the coherence of a turn is evaluated by relating its underlying goals with the interactants' contextual pending goals. If a satisfactory relation cannot be found, a misunderstanding is hypothesized and the goal of reestablishing the intersubjectivity among the interactants is adopted. This goal leads the agent who has received the incoherent turn to reason on the previous context, to understand which agent has misunderstood the other one. The whole processes of interpretation and recovery are based on goal acquisition and the occurrences of self and other repairs are reduced to the same goal-based process. Moreover, by modeling linguistic and domain actions uniformly, we provide a general model of misunderstandings, not limited to linguistic interaction. Our model takes into account the di erent types of ambiguity which may cause a misunderstanding: since the interpretation process is performed in a layered model of dialogue processing, at each level a misinterpretation is possible. So, we can analyze misunderstandings related with the syntactic or semantic interpretation of utterances (including references), with their illocutionary force and with the domain-level and AM plans underlying the speech-acts. 13 14
in Figure 3. See the description of \Try-execute" in section 3. The \Get-to-do" action has been introduced in the AM library to model the strategies adopted by an agent to induce another agent to perform an action for his own sake.
Other researchers have used abductive frameworks for managing the interpretation of utterances [22, 21] but they cover only a subset of the levels at which a misunderstanding can be identi ed in our approach (typically only the illocutionary one). Moreover, some of these works exploit contextual expectations to guide the interpretation process, but they are principally focused on linguistic expectations (e.g. [21]), so that they strictly depend on the occurrence of incoherent turns immediately after the misunderstood turn. For instance, in [21] and [12] only third and fourth turn repairs are analyzed, while our plan-based approach supports a more complex dialogue context, where both object level and problem-solving level expectations are considered; this allows us to analyze self and other-repairs, without imposing strict constraints on the distance between the rst misunderstood turn and the repair turn. Our work also di ers with respect to the others in that most of them do not consider collaboration aspects of dialogue to explain misunderstandings; so, they are resolved by means of strict recovery strategies, like metarules for restructuring the dialogue context [21, 13]. Currently, we don't generate agent behavior,15 so we have not de ned speci c repair strategies; however, we can explain a linguistic repair as a behavior adopted by an agent after he instantiates a \Restructure" action (to realign the participants' dialogue contexts) and he realizes that his partner has misunderstood him. On the other hand, we can explain an uptake by an agent in a fourth turn repair (\Oh, you meant ... ") as a noti cation to his partner that he has corrected his own dialogue interpretation. In a model for the treatment of communication problems, also speaker and hearer misconceptions should be dealt with, since they can cause breakdowns in the communication [20, 15, 23, 7, 13]. Note however that plan misconceptions have been implicitly reduced to the presence of di erent interpretation contexts in all the approaches based on the presence of buggy plan libraries. This suggests that a basic model of misunderstandings can be extended to the treatment of misconceptions, by considering also the buggy plans as alternatives. The interpretation of dialogue by means of our plan-based agent model is implemented in Common Lisp and runs on SUN workstations.
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