In computer science and information systems, speech-act theory has been used to model
human–computer interaction, office work,
multi-agent system communication and other forms of computer-mediated interaction by treating messages as illocutionary actions that change the social state of an interaction rather than merely transmitting data.
Human–computer interaction and dialogue systems Early work on computational models of speech acts in human–computer interaction proposed representing dialogue as a sequence of illocutionary moves, with explicit changes of conversational state associated with each move. In 1991, Morelli, Bronzino and Goethe described a computational speech-act model of human–computer conversations for medical decision support, in which user inputs and system replies are typed as requests, assertions, confirmations and other speech-act categories that drive the underlying expert system. Later work has used speech-act based models to support automated classification and retrieval of conversations: for example, Twitchell
et al. modelled conversations in e-mail and chat as sequences of speech acts in order to classify threads and retrieve segments relevant to particular tasks. More recent natural-language processing research treats speech-act recognition as a text classification problem. Qadir and Riloff, for example, train statistical classifiers to recognise sentences in online message boards as instances of Searle's main speech-act categories (commissives, directives, expressives and representatives). Related work has applied machine-learning methods to classify speech acts in online chat and other forms of computer-mediated communication.
Conversation for action and workflow modelling Another influential application of speech-act theory is the
conversation for action framework developed by Terry Winograd and Fernando Flores in
Understanding Computers and Cognition: A New Foundation for Design. They analyse everyday coordination of work as networks of conversations in which participants make requests, offers, promises and declarations, and represent these conversations using a state-transition diagram that tracks the illocutionary status of each commitment (for example, whether a request has been accepted, fulfilled or declined). In this view, a computer process can track the social state of a transaction—such as which commitments have been made or discharged—even when it does not model in detail the external world that the commitments concern. The conversation-for-action model has influenced
computer-supported cooperative work, workflow management and business process modelling. For example, Medina-Mora
et al. propose
action workflow as an office-automation architecture in which work is coordinated through structured conversations for action, This line of work is sometimes described as the
Language/action perspective on information systems design.
Rules and protocol design In specifying communication protocols for distributed and multi-agent systems, computer scientists have drawn on John Searle's distinction between
regulative and
constitutive rules. Regulative rules prescribe or constrain behaviour in an activity that could in principle exist without them (for example, traffic regulations), whereas constitutive rules do not merely regulate but help to define an activity, such as the rules of games or institutional practices. Within the language–action and multi-agent systems traditions, interaction protocols are often described as constitutive rules that create and shape social realities such as commitments, permissions and institutional facts, rather than as mere constraints on message passing.
Multi-agent systems In
multi-agent systems, communication between software agents is commonly modelled using speech-act labels that express the intended illocutionary force of a message. A message with the performative
inform, for example, is understood as an attempt to add some content to the recipient's knowledge base, whereas a performative such as
request or
query asks another agent to perform an action or provide information. Early agent communication languages such as
KQML and the
FIPA Agent Communication Language define sets of performatives (such as "inform", "request" and "query") together with a formal semantics inspired by Searle's analysis of speech acts. The semantics of KQML and FIPA ACL are often described as
mentalist or
psychological, because they interpret communicative acts in terms of the beliefs, desires and intentions that agents are presumed to have. Munindar P. Singh has argued that such mentalist semantics are ill-suited to open systems, and has instead advocated a
social semantics in which communication creates and manipulates publicly observable social commitments between agents, without making strong assumptions about internal mental states. Andrew J. I. Jones and co-authors have likewise criticised psychological approaches to agent communication, arguing for semantics grounded in social and institutional facts. A later collection of manifestos on agent communication reports a broad consensus in favour of commitment-based social semantics for open multi-agent systems and questions the adequacy of the original FIPA ACL semantics for such settings.
Other uses in technology • An office can be seen as a system of speech acts. The abbreviation
SAMPO stands for
Speech-
Act-based office
Modeling a
ppr
oach, which "studies office activities as a series of speech acts creating, maintaining, modifying, reporting, and terminating commitments". • Speech act profiling and related techniques have been used to detect deception in synchronous
computer-mediated communication, for example by analysing the distribution of different speech-act types in chat and instant-messaging conversations. • Automatic speech-act classification has been applied in a variety of natural-language processing tasks, including the detection of questions, answers and other conversational roles in online discussion fora and chat logs. ==In political science==