Agents: Pros and Cons

Keita Fujii

Jennifer Rhough

PapersAgents that Reduce Work and Information Overload (P. Maes, p. 525-536) Presenting Through Performing: On the Use of Multiple Lifelike Characters in Knowledge-Based Presentation Systems (E. André, T. Rist; IUI-2000, pp. 1-8) Embedding Critics in Design Environments (G. Fischer, 537-561) Multimodal Interaction for Distributed Interactive Simulation (P. Cohen et al., 562-571) Animated Conversation: Rule-based Generation of Facial Expression, Gesture and Spoken Intonation for Multiple Conversational Agents (J. Cassell, J.et al., p. 582-591) Direct manipulation vs. interface agents§§ (B. Shneiderman, P. Maes; Interactions 4, 1997, p. 42-61)

These papers focus on agents that:Support task performance Perform tasks on behalf of users Present informationEnable integration of complex software

systemsCreate interfaces possessing

anthropomorphic communicative abilities integrated speech, facial displays, gesture

Agents that Reduce Work and Information Overload

MotivationWe increasingly use computers for our

everyday activities Increasing number of untrained users

Dominant Interaction MetaphorDirect manipulation vs. Indirect

management

Building Agents

Two problems to overcomeCompetence

How, when and whatTrust

Comfort levels in delegating tasks Integrate into existing interfacesWay of operating should be easily

understandable

Past Approaches

“Semi-autonomous Agents” Example: Email sorting

agent Consists of a collection of

user programmed rules Competence not dealt

with Trust

Do you trust your own skills?

“Knowledge-based approach” Interface agent supplied

with extensive domain specific knowledge

Competence issues Trust issues

Another approach

Hypothesis is that under certain conditions the agent can “program itself”

Two conditions need to be fulfilledUse of application involves repetitive

behaviorThis behavior is potentially different for all

users

Personal Assistant Metaphor

Assists user by: Hiding complexity of difficult tasks Performs tasks on user’s behalf Trains/teaches user Helps different users collaborate Monitors events and procedures

The learning approach

Requires less work from the end-user and application developer

Is a solution to the trust problem Allows agents to reason their behavior Agents can more easily adapt to the user over

time and become customized to individual/organizational preferences and habits

Helps in transferring info, habits, and know-how among the different users of a community.

How the agent acquires competence

Electronic Mail Agent (Maxims)

Learning technique is memory-based learning Learns to prioritize, delete, forward, sort, and

archive mail on behalf of user by “looking over the shoulder” of the user

Agent memorizes generated situation-action pairs Situations described by features

Maxims

Agent will compare new with memorized situations and tries to find a set of nearest neighbors to base its action distance metric – weighted sum of differences for

the features; weight determined by agent agent analyzes its memory for correlation bet

features and actions taken From vs Date

measures confidence in prediction

Maxims

Two user defined thresholds Do-it

Will take action

Tell me Will ask and wait

confirmation

Maxims

Slow start problem user can instruct agent explicitly

default or hard-and-fast rules, use of “wildcard” fields Multi-agent collaboration

Confidence is below “tell-me” so ask other agents by sending part of description via email

Learns trustworthy sources

Preliminary user approval Report feeling comfortable delegating tasks Users want to be able to instruct agent to

disregard behavior

Meeting Scheduling Agent

Same software agent as above but attached to a meeting scheduling package assists user in

scheduling of meetings (accept/reject, schedule, reschedule, negotiate times)

News Filtering Agent

User creates “news agents” and initialize by giving it +/- examples of articles User can give feedback on portions of articles recommended No social filteringLimitations Users rely on it too much - still responsible for

finding less predictably interesting articles Restriction to keywords only, no semantic analysis

Entertainment Selection Agent

Social filtering Relies solely on correlations between different users

Problems Users can rely too much and not enter new

information on items discovered themselves How to jumpstart the system so agents notice

correlations Users can rely too much and not enter new

information on items discovered themselves

Virtual Users

Some questions to ask

How to guarantee user’s privacy?

How can heterogeneous agents collaborate?

Should the user be held responsible for the agent’s actions?

Presenting through Performing: On the Use of Multiple Animated Characters in Knowledge-Based Presentation

Systems

Based on observation that vivid and believable dialogues are a means to present information to an audience

Use of animated characters Ability to express emotions in a believable way Provide means of conveying conversational

signals Users rate presentations by characters as lively

and engaging

Rationale

Presentation teams vs. face to faceEasier to convey differing points of view

Debates between two charactersAllows reinforcementSingle members function as indices to help

user classify informationAlso used to convey meta-information

Some people feel uncomfortable when addressed directly by an agent

Related Work

Virtual human-like weather reporter One agent for presenting information

Bank teller and employee Restricted to Q&A type dialogue between two

agents

Mr. Bengo Resolutions of disputes with judge, prosecutor,

and lawyer (controlled by user) Exhibits basic emotions but not through linguistic

style

Designing Presentation Dialogues

Choose dialogue typeSales dialogue and soccer commentary

Define rolesSales – seller and buyer

Define characters to occupy rolesPersonality and emotional traits

Gestures, linguistic styleDistinguishable by expertise, audio/visual

appearance, interests

Generation of Dialogue

Actors with scripted behaviors Actors in a play Knowledge to be communicated known a priori Possible to vary dialogue by expressions,

gestures, emotions

Autonomous actors Agents draw from dialogue strategies to meet a

certain goal (can be different) Reactive and difficult to ensure coherence

Inhabited Market Place

Inhabited Market PlaceScriptedOrdinary product database each example with

n attributes

Attributes also grouped according to the values of the character safety, economy,

comfort, prestige, environmental considerations, etc

Design of Information Dialogues

Central planning component

Knowledge is represented by plan operators handle the dialogue

and allocation of dialogue agents.

NAME: “DiscussValue1”

GOAL: PERFORM DiscussValue $attribute;

PRECONDITION:

FACT polarity $attribute $dimension “neg”;

FACT difficulty $attribute $dimension “low”;

FACT Buyer $buyer;

FACT Negative $buyer;

FACT Seller $seller;

BODY:

PERFORM NegativeResp $buyer $dimension;

PERFORM RespNegResp $seller $attribute $dimension;

Generation Example

Agent Role Personality factors Interests

Robby seller extravert, agreeable sportiness

Peedy buyer introvert, disagreeable environment

Peedy: How much gas does it consume?

Robby: It consumes 8 liters per 100 km.

Peedy: Isn’t that bad for the environment?;;;negative comment because it is disagreeable, less direct speech

;;;because it is introvert

Robby: Bad for the environment? It has a catalytic converter. It is made of recyclable material.

;;;questions the negative impacts and provides counter arguments

RoboCup Soccer Games

Semi-autonomous agents triggered by events occurring in the scene

& dialogue from other agent rapidly changing environment

Gerd and Matze Characterized by sympathy of team, level

of extraversion, openness, and two emotional dispositions, excitability, and valence

Dialogue Input and Templates

Basic input is obtained by the soccer server delivers player location and orientation, ball

location, score, play modes (goal kicks, throw-ins) info is pieced together at a more conceptual level to

provide material for characters

Templates extracted from 13.5 hours of actual soccer reports and characterized by features like verbosity, bias, formality Selection of template filtered by

situational needs like time remove templates that were recently used keep those that are aligned with character’s attitude keep those aligned with character’s personality

Generation ExampleAgent Attitude Personality factors

Gerd in favor of team Kasunga extravert, open

Matze neutral introvert, not open

Gerd: Kasunga kicks off;;;recognized event: kick off

Matze: Andhill 5;;;recognized event: ball possession, time pressure

Gerd: We’re live from an exciting game, team Andhill in red versus Kasunga in yellow

;;;time for background information

(…)

Gerd: ball hacked away by Kasunga 4;;;recognized event: shot, flowery language since it is creative

ConclusionsTesting Users found the scenarios entertaining and

amusing Eager to test the effect of role castings on the

generated presentation Implies people might learn more about a subject matter

because they are willing to spend more time with a system

Questions How to actively involve the user, either as a co-

presenter or by providing feedback during performance

Optimal number of roles and casting

Embedding Critics in Design Environments

The critiquing approach Growth of human knowledge Helps in error elimination Promotion of mutual understanding of all

participants

Computer based critiquing applied to design Critics recognize and communicate debatable

issues Suited for design tasks where

Knowledge of design domain is incomplete/evolving Design knowledge is distributed Problem requirements can only be partially specified

Shortcomings that hinder the ability to say the “right” thing at the “right” time

Lack of domain orientationInsufficient facilities for justifying critic suggestionsLack of an explicit representation of user’s goalsNo support for different perspectivesTiming problemsPassive vs. active critics

HYDRA-KITCHEN

Design creation tools Construction component

Analogous to the Paint program Includes palette of domain-oriented design units (e.g.

sinks, stoves) Critics are tied to units and relationships between units

Specification component Allows designers to describe abstract characteristics of

their design Dynamic

Used to tailor critic’s suggestions and explanations

HYDRA-KITCHEN IIDesign information repositoriesArgumentative hypermedia component

Consists of issues, answers, and arguments about decisions made in the design

Identifies pros and cons of a suggestion and helps users to understand consequences of following a suggestion

Catalog componentCollection of previously constructed designsCan be used by critics as examples illustrating

solutions

Generic Critics

Enabled by placing design units into the construction area

Reflects knowledge that applies to all designs

Defined through property sheets that specify rules and relations Users can add and modify

Specific critics

Enabled by the partial specification

Fine tune generic critics

Detects inconsistencies between design specification and construction Situation specific physical characteristics

Size/shape of kitchen, owner’s height

Specified requirements Abstract domain concepts like safety or efficiency

Interpretive critics

Enabled by the currently active design perspective

Examines the design from different viewpoints Electrician, plumber, city inspector, interior

designer

Inheritance network - inherit other critics

Can then add additional rules and modify inherited ones

Some advantages

Embedding allows access the work state and time delivery of information that is relevant to the current taskSupport for different perspectivesCritic suggestions supported by domain-oriented design environmentDesign environment allows explicit representation of user’s goalsLocating relevant information Large information space

People are lazy or unaware

HYDRA-KITCHEN Remarks

Learning on demand Integrate learning into work Immediate gratificationRelevant to task

End user modifiability

How to “seed” with domain knowledgeSystem builders not domain experts