software agents in support of human argument mapping

Software Agents in Support of Human Argument Mapping

1 http://creativecommons.org/licenses/by-nc/2.0/uk

3rd International Conference on Computational Modelling of Argument Desenzano del Garda, Italy, 8-10 Sept. 2010

Simon Buckingham Shum

Knowledge Media Institute Open University

Jack Park

Knowledge Media Institute Open University

Maarten Sierhuis

NASA Ames Research Center Technical University of Delft Carnegie Mellon University SV

Matthew Brown

Carnegie Mellon University SV University of Utah

overview the challenge + vision

background: IBIS, Compendium, Brahms

progress to date: human/agent argument mapping + multiagent simulation @NASA

new work: Brahms agent-enabling Compendium

Brahms IBIS-agent simulation of dialogue

future work

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Current tools &

practices for discourse and

problem analysis

Argumentation Theory

COMMA research

?

Our challenge as an applied research discipline

semiformal bridge

Current tools &

practices for discourse and

problem analysis

Argumentation Theory

COMMA research

into logics

? ?

A Human-Centred Computing strategy

Annotation Hypertext

Visualization e-Deliberation

e-Learning UX design

The vision: Computer-Supported Collaborative Argumentation integrated into Work Systems

Agents

Simplified subset of discourse

Modelling & Simulating

Work Systems

Humans Discourse in

authentic work systems

Affordances & Services

The vision: Computer-Supported Collaborative Argumentation integrated into Work Systems

Agents Simplified subset of

discourse

Modelling & Simulating

Work Systems

Humans Discourse in

authentic work systems

Affordances & Services

Fraught with politics, emotion, pressure, information overload, competing agendas, high expertise but poor

argumentation skills and low tolerance of new ICT.

e.g. cases where Compendium has been used: redesigning federal airspace; environmental protection policy; improving Shuttle launch procedures; HIV/AIDS

prevention strategy; participatory urban planning

Help manage attention, coordination and reasoning in a

dynamic environment with information overload

the IBIS notation, Compendium, and mapping practices

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Horst Rittel’s IBIS (1972): Issue-Based Information System

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Compendium Java application:

Nodes can be embedded in multiple maps, can be specialized with Tags, and can link to source documents

>80,000 downloads by >59,000 unique IP numbers Active user community and small developer community

visual hypermedia for managing the connections between ideas formally and informally

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Real-time dialogue/argument mapping

Jeff Conklin, developer of gIBIS and QuestMap,

& Dialogue/Issue Mapping methods www.cognexus.org

Tim van Gelder, developer of Rationale & bCisive & Argument Mapping methods www.austhinkconsulting.com

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Real-time Compendium mapping (Open University Scenario Building)

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Real-time Compendium mapping (NASA Shuttle Launch Control)

Issue/Argument Mapping emerges from the labs

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Online Deliberation: Emerging Tools Workshop Online Deliberation 2010, Leeds UK (30 June – 2 July) www.olnet.org/odet2010

ESSENCE: E-Science, Sensemaking & Climate Change

www.projects.kmi.open.ac.uk/essence

Asynchronous Issue/Argument Mapping (David Price, Debategraph.org)

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Asynchronous Issue/Argument Mapping (Cohere)

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Brahms agent-based work practice modelling

& simulation system 16

Work is like a symphony, Well rehearsed, but always different

Work Practice Modeling

  Groups & Agents   work as activities   beliefs trigger work

  Collaboration between Agents   agents react to and interact with

other agents   same time/same place   same time/different place   different time/same place   different time/different place

Work Practice Modeling (cont/d)

  Tools & Artifacts   tools used in activities   artifacts created in activities

  Environment/Geography   agents have a location   artifacts have a location   detecting real-world facts

  Communication   is situated   the means of communication

depends on the situation   impacts efficiency of work

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Group = Student, Agent = Alex

Geography = Berkeley, CA

Belief = Alex is hungry

Activity = Eating

Workframe = When hungry go eat

Object = Money, Debit card, ATM

Thoughtframe = If no money go to the ATM machine

Brahms Agent Environment http://www.agentisolutions.com

  Composer for building models

  Compiler for compiling models

  Virtual Machine for simulating models

  Agent Viewer for viewing simulations

Mission Control Center, International Space Station: Brahms multiagent Orbital Communications Adaptor Mirroring System [24] Sierhuis, et al., AAMA Conf. 2009

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prior work @NASA

Compendium/Brahms integration

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http://projects.kmi.open.ac.uk/coakting/nasa

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NASA Mobile Agents Field Trials: Simulating an Earth/Mars work system [16, 25]

http://projects.kmi.open.ac.uk/coakting/nasa (view interactive IBIS maps in Safari browser)

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NASA Mobile Agents Field Trials: Simulating an Earth/Mars work system

Scientist (Mars)

Scientist (Earth)

Scientist (Mars)

Scientist (Earth)

Agents (Mars)

Compendium used as a collaboration medium with both humans + agents, reading + writing IBIS maps

Real time “Dialogue Mapping” of NASA science team deliberation (using graphical IBIS, in Compendium)

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NASA Mobile Agents Field Trials Compendium activity plans for surface exploration, constructed by scientists on ‘Earth’, interpreted by software agents on ‘Mars’

The Compendium nodes and relationships in this plan were interpreted by Brahms software agents for monitoring and coordinating astronaut and robot activity during surface explorations.

Copyright, 2004, RIACS/NASA Ames, Open University, Southampton University Not to be used without permission

RST-telecon-2005-04-11.i.avi 1:11:57

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NASA Mobile Agents Field Trials Compendium science data map, generated by software agents, for interpretation by Mars+Earth scientists

The Compendium maps were autonomously created and populated with science data by Brahms software agents that use models of the mission plan, work process, data flow and science data relationships to create the maps.


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NASA testbed: Compendium-based photo analysis by geologists on ‘Mars’


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NASA testbed: Compendium scientific feedback map from Earth scientists to Mars colleagues


New Development 1

adding Brahms Agents to connect distributed Compendium clients

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Compendium-Brahms Architecture

Brahms Agent VM

Compendium-Brahms Use Cases

  User generates query seeking nodes in remote map databases

  Brahms VM accepts query   Brahms VM broadcasts query   Remote Brahms VM passes query to Compendium

Adaptor   Compendium Adaptor queries database   Compendium Adaptor returns query results to Brahms

VM   Query result returned to calling agent   User selects results   Results added to user’s Dialogue/Argument Map

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User Generates Query

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Query

Returned Results

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Dialogue Map with Query Results added

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New Development 2

extending Brahms agents to conduct IBIS

conversations

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How to enable agents to conduct IBIS conversations?

  The map ≠ the discussion for humans, but for agents, the map = the discussion

  IBIS Dialogue Mapping benefits from human intelligence to take turns, summarise and link utterances — but agents can respond simultaneously and identically, potentially resulting in duplicate nodes

  Thus, there is need for a facilitator agent to maintain the structure of the argumentation structure and ensure there are no duplicate nodes

IBIS Agent Interfaces

IBISParticipantAgent preArgumentationActivity()

  Defines the actions taken by an agent before the argumentation begins, this may include the sending of the initial IBIS nodes that start the argumentation

postArgumentationActivity()   Defines the actions taken by an agent after the argumentation has

concluded, this may include deciding the outcome of the argumentation

processQuestionNode(IBISNode node), processIdeaNode(IBISNode node), processProNode(IBISNode node), processConNode(IBISNode node)

  Defines the actions taken by an agent when processing the various types of IBIS nodes, this may include the creation of new beliefs and/or responding with an IBIS node

IBIS Agent Interfaces (continued)

IBISFacilitatorAgent   checkForDuplicate(IBISNode node)

  Defines the process by which IBIS nodes are determined to be unique or duplicate

Brahms IBIS-agent simulation of CCFP

deliberation

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Collaborative Convective Forecast Product (CCFP)

As a consensus forecast created through online textchat by

meteorologists representing different organizations

Example CCFP, used for FAA strategic planning around severe weather.

AWC Forecaster leads, presenting their forecast for

discussion

Textchat shown to produce inefficient

dialogue, motivating agent simulation as

IBIS moves

CCFP Chat Scenario Setup

  Simulation consists of:   Agents representing CCFP chat participants (ZNY, ZDI,

AWCForecaster)   Each agent has initial beliefs about the weather forecaster   ZNY and ZDI have the ability to voice their disagreement with

AWCForecaster's initial forecast

  Facilitator agent (CCFPFacilitatorAgent)

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CCFP Chat Example: Brahms agents’ beliefs about WeatherEvent1 agent AWCForecaster memberof CCFPChatLeader{ initial_beliefs: (WeatherEvent1.name = "weather_event_1"); (WeatherEvent1.confidence = 1); (WeatherEvent1.growth = 1); (WeatherEvent1.tops = 1); (WeatherEvent1.coverage = 1); (WeatherEvent1.speed = 25); (WeatherEvent1.direction = 45); } agent ZID memberof CCFPChatParticipant{ initial_beliefs: (WeatherEvent1.name = "weather_event_1"); (WeatherEvent1.confidence = 1); (WeatherEvent1.growth = 1); (WeatherEvent1.tops = 2); (WeatherEvent1.coverage = 2); (WeatherEvent1.speed = 25); (WeatherEvent1.direction = 45); } agent ZNY memberof CCFPChatParticipant{ initial_beliefs: (WeatherEvent1.name = "weather_event_1"); (WeatherEvent1.confidence = 2); (WeatherEvent1.growth = 2); (WeatherEvent1.tops = 2); (WeatherEvent1.coverage = 1); (WeatherEvent1.speed = 25); (WeatherEvent1.direction = 45); }

Human-readable Compendium IBIS map of the CCFP Chat simulation, given the preceding beliefs

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future work

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Exponential growth in runtime as IBIS nodes are added — algorithm optimization required!

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The Vision… Agents as described, augmenting work by integrating discourse with work system models

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Agents drawing on known constraints and arguments in a dynamic work practice environment, e.g.

the location of people or artifacts

the availability of resources or communication channels

the argumentation schemes on which decisions may depend

relevant other conversations/analyses

improved tools for filtering overwhelming information

Hypermedia Discourse Project articles, software, books, news, movies, community…

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CompendiumInstitute

http://projects.kmi.open.ac.uk/hyperdiscourse

software agents in support of human argument mapping

Technology