King's College London, 2019

1. Research Overview
THuMP themes
Dr. Martin Chapman
King’s College London martin.chapman@kcl.ac.uk

2. Themes in THuMP
1. Software infrastructure.
• Consult decision support system (DSS).
2. Human-machine interaction.
• Consult chatbot.
• Distributed Research Games (DRGs).
3. Resource allocation.
• CAT (Trading Agent Competition, reversed) platform.
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3. 1. Software Infrastructure.
Consult: DSS to assist patients who have suffered a stroke to
self-manage their treatment1
:
Blood pressure
(Nokia)
Pulse
(Garmin)
Activity
(Garmin)
Heart Rate / ECG
(VitalPatch)
EHR
(EMIS)
Device
Integration
(Nokia)
Device
Integration
(Garmin)
Sensor-FHIR
mapper
EHR Integration
(EMIS)
EHR-FHIR
mapper
HAPI FHIR
Message
Passer
Dialogue
Manager
Provenance
Data
Miner
Argumentation
Engine
Drug
Interactions
Tablet
Browser
PC
Browser
Chat
Server
UI backend
Sensor data
Sensor
data
FHIR resources
FHIR resources
FHIR resources
Processed
patient data
Patient
data
Fragment
Processed
patient data
Processed patient data,
goal
Results
Dialogue responses
Interactions
Data summaries Results
Wearable
EHR
PIMS
DNC
Argumentation
Focus: Connection of individual processing components, integration
of data sources and user interaction.
1Martin Chapman et al. “Computational Argumentation-based Clinical Decision
Support (Demonstration)”. Accepted, AAMAS, 2019.
2

4. 2. Human-machine interaction.
1. Consult: Implementing a chatbot to interact with patients.
• Using argumentation to initially determine which types of
interactions to support (e.g. information-seeking [4]).
2. Supporting the interaction of human participants with a game
system (a DRG) in order to elicit network attack and defence
strategies2
.
2Martin Chapman. “Cyber Hide-and-Seek”. PhD thesis. King’s College London, 2016.
3

5. 3. Resource Allocation.
CAT platform: another DRG designed to evaluate user-designed
markets by determining their attractiveness to autonomous traders.
Contribution: introducing exchange of advice between traders3
.
Problem:
conflicting advice.
Framing: who do I
allocate my choice
of market to?
def reward ( trader , p r o f i t ) :
rewards [ trader ] = p r o f i t
def selectTrader ( ) :
i f random ( ) > epsilon :
return max( rewards )
else :
return random ( traders )
Solution: ϵ-greedy.
3Martin Chapman et al. “Social networking and information diffusion in automated
markets”. In: TADA-AMEC (AAMAS). 2012, pp. 1–14.
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6. References
Martin Chapman. “Cyber Hide-and-Seek”. PhD thesis. King’s
College London, 2016.
Martin Chapman et al. “Computational Argumentation-based
Clinical Decision Support (Demonstration)”. Accepted, AAMAS,
2019.
Martin Chapman et al. “Social networking and information
diffusion in automated markets”. In: TADA-AMEC (AAMAS). 2012,
pp. 1–14.
Elizabeth I Sklar and M Q Azhar. “Argumentation-Based
Dialogue Games for Shared Control in Human-Robot Systems”.
In: Journal of Human-Robot Interaction 4.3 (2015), p. 120.
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