Software Agents in Support of Human Argument Mapping Abstract. This paper reports progress in realizing human-agent argumentation, which we argue will be part of future Computer-Supported Collaborative Argumentation (CSCA) tools. With a particular interest in argument mapping, we present two investigations demonstrating how a particular agent-oriented language and architecture can augment CSCA: (i) the use of the IBIS formalism enabling Brahms agents to simulate argumentation, and (ii) the extension of the Compendium tool by integrating it with Brahms agents tasked with detecting related discourse elsewhere. Keywords. Argument Mapping, IBIS, Compendium, Brahms, Multi-Agent Systems 3rd International Conference on Computational Modelling of Argument Desenzano del Garda, Italy, 8-10 Sept. 2010

1. 3rd International Conference on Computational Modelling of Argument
Desenzano del Garda, Italy, 8-10 Sept. 2010
Software Agents in
Support of Human
Argument Mapping
Simon Buckingham Shum Maarten Sierhuis
Knowledge Media Institute NASA Ames Research Center
Open University Technical University of Delft
Carnegie Mellon University SV
Jack Park Matthew Brown
Knowledge Media Institute Carnegie Mellon University SV
Open University University of Utah
http://creativecommons.org/licenses/by-nc/2.0/uk 1

2. 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
2

3. Our challenge as an applied research discipline
Current
Argumentation
tools &
Theory
practices for ?
discourse and
COMMA
problem
research
analysis

4. A Human-Centred Computing strategy
Current Annotation Argumentation
tools & Hypertext Theory
practices for Visualization
discourse and e-Deliberation COMMA
problem e-Learning research
analysis UX design into logics
? semiformal ?
bridge

5. The vision: Computer-Supported Collaborative
Argumentation integrated into Work Systems
Humans Agents
Discourse in
authentic work Simplified subset
systems of discourse
Affordances Modelling &
& Services Simulating
Work Systems

6. The vision: Computer-Supported Collaborative
Argumentation integrated into Work Systems
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: Help manage attention,
redesigning federal airspace; environmental protection coordination and reasoning in a
policy; improving Shuttle launch procedures; HIV/AIDS dynamic environment with
prevention strategy; participatory urban planning information overload
Humans Agents
Discourse in
authentic work Simplified subset of
systems discourse
Affordances Modelling &
& Services Simulating
Work Systems

7. the IBIS notation,
Compendium, and
mapping practices
7

8. Horst Rittel’s IBIS (1972):
Issue-Based Information System
8

9. Compendium Java application:
visual hypermedia for managing the connections
between ideas formally and informally
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 9

10. Real-time dialogue/argument mapping
Jeff Conklin, Tim van Gelder,
developer of gIBIS and QuestMap, developer of Rationale & bCisive
& Dialogue/Issue Mapping methods & Argument Mapping methods
www.cognexus.org www.austhinkconsulting.com
10

11. Real-time Compendium mapping
(Open University Scenario Building)
11

12. Real-time Compendium mapping
(NASA Shuttle Launch Control)
12

13. Issue/Argument Mapping emerges from the labs
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
13

14. Asynchronous Issue/Argument Mapping
(David Price, Debategraph.org)
14

15. Asynchronous Issue/Argument Mapping (Cohere)
15

16. Work is like a symphony,
Well rehearsed, but
always different
Brahms agent-based
work practice modelling
& simulation system 16

17. 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

18. 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
18

19. 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

20. 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

21. Mission Control Center, International Space
Station: Brahms multiagent Orbital Communications Adaptor
Mirroring System [24] Sierhuis, et al., AAMA Conf. 2009
21

22. prior work @NASA
Compendium/Brahms
integration
http://projects.kmi.open.ac.uk/coakting/nasa
22

23. 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)
23

24. NASA Mobile Agents Field Trials:
Simulating an Earth/Mars work system
Compendium used as a collaboration medium with both
humans + agents, reading + writing IBIS maps
Agents (Mars)
Scientist Scientist Scientist Scientist
(Earth) (Earth) (Mars) (Mars)
24

25. Real time “Dialogue Mapping” of NASA science
team deliberation (using graphical IBIS, in Compendium)
25

26. NASA Mobile Agents Field Trials
Compendium activity plans for surface exploration, constructed by scientists
on ‘Earth’, interpreted by software agents on ‘Mars’
Copyright, 2004, RIACS/
NASA Ames, Open
University, Southampton
University
Not to be used without
permission
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. 26
RST-telecon-2005-04-11.i.avi
1:11:57

27. NASA Mobile Agents Field Trials
Compendium science data map, generated by software agents, for
interpretation by Mars+Earth scientists
Copyright, 2004, RIACS/
NASA Ames, Open
University, Southampton
University
Not to be used without
permission
The Compendium maps were autonomously created and populated with science data by Brahms software agents that use models of the 27
mission plan, work process, data flow and science data relationships to create the maps.

28. NASA testbed:
Compendium-based photo analysis by geologists on ‘Mars’
Copyright, 2004,
RIACS/NASA Ames,
Open University,
Southampton
University
Not to be used
28
without permission

29. NASA testbed:
Compendium scientific feedback map from Earth scientists to Mars
colleagues
Copyright, 2004,
RIACS/NASA Ames,
Open University,
Southampton
University
Not to be used
29
without permission

30. New Development 1
adding Brahms Agents
to connect distributed
Compendium clients
30

31. Compendium-Brahms Architecture
Brahms Agent
VM

32. 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
32

33. User Generates Query
Query
33

34. Returned Results
34

35. Dialogue Map with Query Results added
35

36. New Development 2
extending Brahms
agents to conduct IBIS
conversations
36

37. 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

38. 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

39. IBIS Agent Interfaces (continued)
IBISFacilitatorAgent
 checkForDuplicate(IBISNode node)
 Defines the process by which IBIS nodes are determined to be unique
or duplicate

40. Brahms IBIS-agent
simulation of CCFP
deliberation
40

41. Collaborative Convective Forecast Product (CCFP)
Example CCFP, used
for FAA strategic
planning around
severe weather.
As a consensus forecast created
through online textchat by
meteorologists representing
AWC Forecaster different organizations
leads, presenting
their forecast for
discussion
Textchat shown to
produce inefficient
dialogue, motivating
agent simulation as
IBIS moves

42. 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)

43. 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); 43
}

44. Human-readable Compendium IBIS map of the
CCFP Chat simulation, given the preceding beliefs
44

45. future work
45

46. Exponential growth in runtime as IBIS nodes are
added — algorithm optimization required!
46

47. The Vision…
Agents as described, augmenting work by
integrating discourse with work system models
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
47

48. Hypermedia Discourse Project
articles, software, books, news, movies, community…
http://projects.kmi.open.ac.uk/hyperdiscourse
Compendium
Institute
48