Mixed-Initiative Dialogue Systems for Collaborative Problem-Solving

Mixed-Initiative Dialogue Systems for Collaborative Problem-Solving George Ferguson & James Allen University of Rochester

Motivation • Eric Horvitz on “mixed-initiative” (2000): • I shall use the phrase to refer broadly to methods that explicitly support an efficient, natural interleaving of contributions by users and automated services aimed at converging on solutions to problems. • Natural language dialogue systems for mixed-initiative problem solving • Efficient: easy to say complicated things • Natural: no training • Emphasizes role of the user

Goals • Describe an architecture for mixed-initiative dialogue systems such that: • The system is able to do many things (in parallel) • e.g., search the web, plan activities, learn new tasks, observe its environment, ... • Dialogue-based interaction with a user is one of those things (albeit an important one) • Dialogue is in service of collaboration--we talk together in order to work together to solve problems • Collaboration is driven by principles of collaborative activity (joint intention) • Initiative (and mixed-initiative interaction) arises naturally from agents managing their joint intentions

Outline • Architecture of Mixed-Initiative Dialogue Systems • Dialogue Systems • Agents • Collaborative Agents • Components of Collaboration • Ontology of collaborative problem solving acts • User- and system-initiative APIs • Extended Example • User initiative • Interpretation • System initiative • Collaborative behavior • Proposals • Agreement • Other Issues

Dialogue Systems:The Standard Approach • A standard dialogue system is not an agent

BDI Agents (briefly) Goal AssessmentGoal PrioritizationUtility Assessment... Hand-coded ProceduresReactive ControlLearned PolicyAdaptive PolicyMeans-Ends PlanningInteractive Control... Beliefs Desires Intentions What to do? Execute Observe

Architecture of a Collaborative Dialogue Agent • Overall behavior controlled by Task Manager • Dialogue behavior under Dialogue Manager’s control • Dialogue Manager components operate independently and asynchronously • All components use shared BDI knowledge base (KB)

Collaboration Manager Interpretation Generation • Ontology of collaborative problem solving acts • User initiative: intentions from CM to TM • System initiative: intentions from TM to CM CPS Act Ontology Collaboration Manager User Initiative API System Initiative API JointIntentions Task Manager

Collaborative Problem Solving Act Ontology Interpretation Generation • Ontology of collaborative acts that an agent can perform • Propose, Accept, Reject, Report, Check, Ask • Modality-independent • Uses BDI language for content • Bel, Des, Commit • “Know-ref” or “Know what is” forms • e.g. “Let’s buy a book” • (Propose USR SYS (Commit (USR SYS) (Do PURCHASE123)))where PURCHASE123 has object = BOOK456, ... Collaboration Manager CPS Act Ontology User Initiative API System Initiative API JointIntentions Task Manager

User Initiative API Interpretation Generation • User utterances interpreted using BDI state and principles of collaborative activity • Under the right conditions, user initiative results in suggestion that the Task Manager adopt a joint intention • Decision to adopt is up to the agent (Task Manager), not the dialogue subsystem • Asynchronous accept/reject from TM drives dialogue • If accepted, new intention drives agent behavior CPS Act Ontology Collaboration Manager User Initiative API System Initiative API JointIntentions Task Manager

System Initiative API Interpretation Generation • Arises from Task Manager assigning a collaborative goal (joint intention) to Collaboration Manager • CM determines dialogue behavior based on BDI state and principles of collaborative activity • TM asynchronously informed of success or failure CPS Act Ontology Collaboration Manager User Initiative API System Initiative API JointIntentions Task Manager

Example Dialogue USER: I need to purchase an LCD projector for my class. I need XGA resolution and it has to be portable. Find an appropriate machine and get approval to purchase it by this afternoon. SYSTEM: Ok. What is the budget? USER: 700 dollars. SYSTEM: Ok. ... SYSTEM: I’ve found three projectors that match your specifications. [displays table] USER: Let’s go with the Hitachi. ... SYSTEM: There’s a problem with the projector purchase. It’s 4 p.m. and I haven’t yet received approval from Fred. USER: You don’t need to get approval if the cost is less than 1000 dollars. SYSTEM: Ok.

User Initiative (1) USER: I need to purchase an LCD projector for my class. • Direct report of a want or need: (Report USR SYS (Des USR (Done PURCHASE123))) • Response: “Thanks for letting me know.”

User Initiative (2) USER: I need to purchase an LCD projector for my class. • Statement of a goal being pursued independently: (Report USR SYS (Commit USR (Done PURCHASE123))) • Response: “Good luck with that.”

User Initiative (3) USER: I need to purchase an LCD projector for my class. • Proposal that a joint commitment (goal) be adopted: (Propose USR SYS (Commit (USR SYS) (Done PURCHASE123))) • If adopted, drives collaborative behavior

Supporting Interpretation • Not the focus of this paper but... • Crucial for natural language • Also crucial for understanding actions of other agents • Need to interpret others’ actions/utterances consistently with one’s beliefs and principles of collaborative activity • We use the same procedures that drive the agent’s collaborative behavior in reverse to help interpret the user’s utterances • Effectively, interpret it as a given CPS act if we might have performed that act given the current BDI state

User Initiative API • User proposals handled as suggestions that the system adopt a joint commitment: (suggest (Commit (USR SYS) (Done PURCHASE123)) • If accepted: “Ok.” • And new commitment drives subsequent behavior • If rejected: “No. ...”

Reasoning Requirements for User Initiative • Have to be able to decide whether to adopt new commitments • Have to to be able to do this for oneself anyway • Some strategies for committing to goals: • Hardcode goals that are acceptable • If I know a way of achieving the goal (by myself or collaboratively), then adopt it • If achieving the goal is not incompatible with my beliefs, desires, and intentions, then adopt it • Can take initiative to gather information necessary for decision • Response to dialogue sub-system is asynchronous • See next section...

System Initiative • Knowing when collaboration is necessary • Collaborative dialogue behavior • Proposals • Agreement

Knowing When Collaboration Is Necessary • Task Manager needs to know what aspects of a task require joint commitment • Hard-coded into task models • Compiled into procedures by combining task models with general principles of collaboration • Incremental meta-decision of execution system • ... • For our example, assume it knows that we need to agree on the budget for purchasing the projector • Perhaps other aspects it can decide on its own • Perhaps have to agree on what aspects need to be agreed!

System Initiative API • Task Manager dispatches collaborative goal to Collaboration Manager: (Commit-What-Is (USR SYS)(the budget of PURCHASE123)) • Collaborative goals drive dialogue behavior

Collaborative Dialogue Behavior • Collaboration Manager is an agent that achieves its collaborative goals by performing CPS acts (leading to dialogue behavior) • Reactive procedures use BDI state to select CPS acts • These rules are compiled versions of the axioms defining the CPS acts ala Cohen & Levesque • For our example, it decides to: (RFP SYS USR (the budget of PURCHASE123))) • Generation produces: “What is the budget?”

Proposals • Suppose the user answers: “700 dollars” • Context leads to interpretation as proposal: (Propose USR SYS (Commit-What-Is (USR SYS) (the budget of PURCHASE123) (* 700 dollars))) • Handled uniformly as suggestion that Task Manager adopt the commitment

Agreement • Generation produces: “OK” • Collaboration Manager execution will now realize that the goal of reaching agreement on a value for the budget has been achieved • Reports successful completion of the goal to the Task Manager • Shared BDI KB contains the new knowledge about the budget

Some Other Issues (1) • Is this only about natural language? • No. • But need an interface where: • Content is explicitly represented • Actions are represented as CPS acts • Seems like A Good Thing in any event

Some Other Issues (2) • Isn’t there more to collaboration than dialogue? • Yes. • But dialogue is useful enough (and hard enough) to be worth focusing on • Our approach embeds dialogue in a general theory of collaborative activity

Conclusions • An architecture for mixed-initiative problem solving assistants • Particular emphasis on dialogue systems • True mixed-initiative system • Goals and commitments come from either party • The system’s collaborative behavior is driven by a formal model of collaborative activity • Which also supports interpretation of user’s behavior • Completely domain- and application-independent • But very knowledge-intensive

Conclusions • A practical way to build collaborative dialogue systems in many domains • Logistics planning • Personal health care • Command and control • Agent team coordination • Office assistant • NL transcription and knowledge mining • Crisis management • ...

For More Information... George Ferguson ferguson@cs.rochester.edu James Allen james@cs.rochester.edu http://www.cs.rochester.edu/~ferguson/ http://www.cs.rochester.edu/research/cisd/

Mixed-Initiative Dialogue Systems for Collaborative Problem-Solving