Dialogue Systems: Simulations or Interfaces?

1. Dialogue Systems: Simulations or Interfaces? Staffan Larsson Göteborg University Sweden

2. Introduction

3. Basic question • What is the goal of formal dialogue research? • Formal dialogue research = • formal research on the semantics and pragmatics of dialogue

4. Two possible answers • Engineering view: the purpose of formal dialogue research is • interface engineering (services and technologies) • enable building better human-computer interfaces • Simulation view: the ultimate goal of formal dialogue research is • a complete formal and computational (implementable) theory of human language use and understanding

5. The convergence assumption There is an extensive if not complete overlap between the simulation of human language use and the engineering of conversational interfaces.

6. Aim of this presentation • Review an argument against the possibility of human-level natural language understanding in computers (simulation view) • Explicitly apply this argument to formal dialogue research, arguing that the covergence assumption is dubious • Draw out the consequences of this for formal dialogue research

7. Formal dialogue research and GOFAI

8. The Turing test • Can a machine think? Turing offers an operational definition of the ability to think • Turing’s imitation game • Test person A has a dialogue (via a text terminal) with B. • A:s goal is to decide whether B is a human or a machine • If B is a machine and manages to deceive A that B is a human, B should be regarded as able to think

9. The Turing test and the Simulation view • The Turing Test can be seen as the ultimate test of a simulation of human language use • The ability to think is operationalised as the ability to carry out a natural language dialogue in a way that is indiscernible from that of a human • The goal of formal dialogue research coincides with the goal of AI (as originally perceived)

10. GOFAI • Artificial Intelligence • Goal: simulate human/intelligent behaviour/thinking • Weak AI:Machines can be made to act as if they were intelligent • Until the mid-80’s, the dominating paradigm of AI was the idea that thinking is, essentially, symbol manipulation • The physical symbol hypothesis • All intelligent behaviour can be captured by a system that reasons logically from a set of facts and rules that describe the domain • This is sometimes referred to as GOFAI • (Good Old Fashioned AI)

11. Dialogue systems and GOFAI • Since around the mid-80’s, GOFAI has been abandoned by many (but not all) AI researchers • Instead, focus on NEFAI (New-Fangled AI) • connectionism, • embodied interactive automata, • reinforcement learning, • probabilistic methods, etc. • However, a large part of current dialogue systems research is based on the GOFAI paradigm • Information States, for example… • Formal pragmatics is often used as a basis for the implementation of dialogue managers in GOFAI-style approaches

12. Formal semantics and GOFAI • GOFAI and formal semantics deals, to a large extent, with similar problems and use similar methods • Formal symbolic representations of meaning • Natural Language Understanding as symbol manipulation • (Even though many early GOFAI researchers appear oblivious to the existence of formal semantics of natural language in the style of Montague, Kamp etc.) • Formal semantics perhaps not originally intended to be implemented, and not as part of AI • Still, formal semantics shares with GOFAI rests on the assumption that natural language meaning can be captured in formal symbol manipulation systems

13. Why GOFAI? • Why GOFAI in formal semantics and pragmatics? • It seems to be the most workable method for the complex problems of natural language dialogue • Natural language dialogue appears to be useful for improving on current human-computer interfaces • But is GOFAI-based research also a step on the way towards ”human-level” natural language understanding in computers, i.e. simulation?

14. Phenomenological arguments against GOFAI

15. Some problems in AI • Frame problem • updating the “world model” • knowing which aspects of the world are relevant for a certain action • Computational complexity in real-time resource-bounded applications • Planning for conjunctive goals • Plan recognition • Incompleteness of general FOL reasoning • not to mention modal logic • Endowing a computer with the common sense of a 4-year-old • AI is still very far from this

16. Humans don’t have problems with these things • Is it possible that all these problems have a common cause? • They all seem to be related to formal representations and symbol manipulation

17. Background and language understanding • Dreyfus, Winograd, Weizenbaum • Human behaviour based on our everyday commonsense background understanding • allows us to experience what is currently relevant, and deal with tings and people • crucial to understanding language • involves utterance situation, activity, institution, cultural setting, ...

18. Dreyfus argues that the background has the form of dispositions, or informal know-how • Normally, ”one simply knows what to do” • a form of skill rather than propositional knowing-that • To achieve GOFAI, • this know-how, along with interests, feelings, motivations, social interests, and bodily capacities that go to make a human being,... • ... would have to be conveyed to the computer as knowledge in the form of a huge and complex belief system

19. CYC (Lenat) and natural language • An attempt to formalise common sense • The kind of knowledge we need to understand NL • using general categories that make no reference to specific uses of the knowledge • Lenat’s ambitions: • it’s premature to try to give a computer skills and feelings required for actually coping with things and people • L. is satisfied if CYC can understand books and articles and answer questions about them

20. “The background cannot be formalised” • There are no reasons to think that humans represent and manipulate the background explicitly, or that this is possible even in principle • “...understanding requires giving the computer a background of commons sense that adult humans have in virtue of having bodies, interacting skilfully with the material world, and being trained into a culture” • Why does it appear plausible that the background could be formalised knowing-that? • Breakdowns • Skill acquisition

21. Skills and formal rules • When things go wrong - when we fail – there is a breakdown • In such situations, we need to reflect and reason, and may have to learn and apply formal rules • but it is a mistake to • read these rules back into the normal situation and • appeal to such rules for a causal explanation of skilful behaviour

22. Dreyfus’ account of skill acquisition 1. Beginner student: Rule-based processing learning and applying rules for manipulating context-free elements There is thus a grain of truth in GOFAI 2. Understanding the domain; seeing meaningful aspects, rather than context-free features 3. Setting goals and looking at the current situation in terms of what is relevant 4. Seeing a situation as having a certain significance toward a certain outcome 5. Expert: The ability of instantaneously selecting correct responses (dispositions)

23. There is no reason to suppose that the beginner’s features and rules (or any features and rules) play any role in expert performance • That we once followed a rule in tying our shoelaces does not mean we are still following the same rule unconsciously • ”Since we needed training wheels when learning how to ride a bike, we must now be using invisible training wheels.” • Human language use and cognition involves symbol manipulation, but is not based on it

24. Recap • Language understanding requires access to human background understanding • This background cannot be formalised • Since GOFAI works with formal representations, GOFAI systems will never be able to understand language as humans do

25. Simulation and NEFAI

26. What about NEFAI? • This argument only applies to GOFAI! • A lot of modern AI is not GOFAI • New-Fangled AI (NEFAI) • interactionist AI (Brooks, Chapman, Agre) • embodied AI (COG) • connectionism / neural networks • reinforcement learning • So maybe human language use and understanding could be simulated if we give up GOFAI and take up NEFAI? • Note that very few have tried this in the area of dialogue • Simply augmenting a GOFAI system with statistics is not enough

27. Progress? • Although NEFAI is more promising than GOFAI... • ... most current learning techniques rely on the previous availability of explicitly represented knowledge – the training data must be interpreted and arranged by humans • in the case of learning the background, this means that the background has to be represented before it can be used for training • But as we have seen, Dreyfus argues that commonsense background cannot be captured in explicit representations

28. Russel & Norvig, in Artificial Intelligence -A Modern Approach (1999) • In a discussion of Dreyfus’ argument: • ”In our view, this is a good reason for a serious redesign of current models of neural processing .... There has been some progress in this direction.” • But no such research is cited • So R & N admit that this is a real problem. In fact it is still the exact same problem that Dreyfus pointed out originally • There is still nothing to indicate that Dreyfus is wrong when arguing against the possibility of getting computers to learn commonsense background knowledge

29. But let’s assume for the moment that the current shortcomings of NEFAI could be overcome... • that learning mechanisms can be implemented who learn in the same way humans do • and that appropriate initial structure of these systems can be given • and that all this can be done without providing predigested facts that rely on human interpretation

30. Some factors influencing human language use • Embodiment • having a human body, being born and raised by humans • Being trained into a culture • by interacting with other humans • Social responsibility • entering into social commitments with other people

31. What is needed to achieve simulation? • So, perhaps we can do real AI, provided we can build robot infants that are raised by parents and socialised into society by human beings who treat them as equals • This probably requires people to actually think that these AI systems are human • These systems will have the same ethical status as humans • If we manage to do it, is there any reason to assume that they would be more useful to us than ordinary (biological) humans? • They are no more likely to take our orders...

32. It appears that the research methods required for simulation are rather different from those required for interface design • The convergence assumption appears very dubious

33. Formal dialogue research and dialogue systems design

34. Consequences of the argument for the engineering view • If we accept the argument that “the background is not formalisable” and that computers (at least as we know them) cannot simulate human language understanding... • ...what follows with respect to the relations between • Formal semantics and pragmatics of dialogue • Non-formal theories of human language use • Dialogue systems design as interface engineering • Both (1) and (2) are still relevant to (3)

35. Winograd on language and computers • Even though computers cannot understand language in the way humans can... • ...computers are nevertheless useful tools in areas of human activity where formal representation and manipulation is crucial • e.g. word processing. • In addition, many practical AI-style applications do not require human-level understanding of language • e.g. programming a VCR, getting timetable information • In such cases, it is possible to develop useful systems that have a limited repertoire of linguistic interaction. • This involves the creation of a systematic domain

36. Systematic domains • A systematic domain is a set of formal representations that can be used in a computer system • Embodies the researcher’s interpretation of the situation in which the system will function. • Created on the basis of regularities in conversational behaviour (“domains of recurrence”)

37. so... • For certain regular and orderly activities and language phenomena... • ... it is possible to create formal representations which capture them well enough to build useful tools • Formal dialogue research can be regarded as the creation of systematic domains in pragmatics and semantics of dialogue

38. Formal semantics and pragmatics of dialogue as systematic domains • Formal theories of language use should be regarded as • the result of a creative process of constructing formal representations (systematic domains) • based on observed regularities in language use • These theories can be used in dialogue systems to enable new forms of human-machine interaction

39. Formal pragmatics • Pragmatic domains include e.g. • turntaking, feedback and grounding, referent resolution, topic management • Winograd gives dialogue game structure as a prime example of a systematic domain • Analysed along the lines of “dialogue games” encoded in finite automata • ISU update approach is a variation of this, intended to capture the same regularities in a (possibly) more flexible way • It is likely that useful formal descriptions can be created for many aspects of dialogue structure

40. Formal semantics • Not a focus of Winograd’s formal analysis, • presumably because Winograd believes that language understanding is not amenable to formal analysis • However, even if one accepts the arguments such as those above... • ... it seems plausible that the idea of systematic domains also applies to semantics • That is, for certain “semantically regular” task domains it is indeed possible to create a formal semantics • e.g. in the form of a formal ontology and formal representations of utterance contents • This formal semantics will embody the researcher’s interpretation of the domain

41. Relevant issues related to semantic domains • How to determine whether (and to what extent) a task domain is amenable to formal semantic description • How to decide, for a given task domain, what level of sophistication is required by a formal semantic framework in order for it to be useful in that domain • In some domains, simple feature-value frames may be sufficient while others may require something along the lines of situation semantics, providing treatments of intensional contexts etc. • Fine-grainedness and expressivity of the formal semantic representation required for a domain or group of domains • e.g. database search, device programming, collaborative planning, ... • Creation of application-specific ontologies • How to extract applications ontologies from available data of the domain, e.g. transcripts of dialogues.

42. but... • Even though some aspects of language use may indeed be susceptible to formal description • This does not mean that human language use actually relies on such formal descriptions represented in the brain or elsewhere • So implementations based on such formalisations are not simulations of human language use and cognition

43. Limits of formalisation • Formalisation will only be useful in areas of language use which are sufficiently regular to allow the creation of systematic domains • So, repeated failures to formally capture some aspect of human language may be due to the limits of formal theory when it comes to human language use, rather than to some aspect of the theory that just needs a little more tweaking.

44. Non-formalisable language phenomena • For other activities and phenomena, it may not possible to come up with formal descriptions that can be implemented • e.g. human language understanding in general, since it requires a background which cannot be formalised • also perhaps aspects of implicit communication, conversational style, politeness in general, creative analogy, creative metaphor, some implicatures • This does not mean that they are inaccessible to science. • They can be described non-formally and understood by other humans • Their general abstract features may be formalisable

45. Usefulness of non-formal theory • Non-formal theories of human language use are still useful for dialogue systems design • Dialogue systems will need to be designed on the basis of theories of human language • They will, after all, interact with a human • May also be useful to have human-like systems (cf. Cassell) • This does not require that implementations of these theories have to be (even partial) simulations of human language use and cognition • Also, observations of human-human dialogue can of course be a source of inspiration for dialogue systems design

46. Conclusions

47. In important ways the simulation view and the engineering view are different projects requiring different research methods • For the simulation project, the usefulness of systems based on formal representations is questionable • Instead, formal dialogue research can be regarded as the creation of systematic domains that can be used in the engineering of flexible human-computer interfaces • In addition, non-formal theory of human language use can be useful in dialogue systems design

48. If interface engineering is liberated from concerns related to simulation... • ...it can instead be focused on the creation of new forms of human-computer (and computer-mediated) communication... • ... adapting to and exploring the respective limitations and strengths of humans and computers.

49. fin

50. Other views of what FDR is