3. Challenge 3
To make effective use of operator cognitive capacity, particularly by human-
machine teaming
Key points for the Land Environment
• considerable improvements need to be made in the interaction between people
and systems
• develop approaches that enable collaborative decision making and
intelligence analysis to support planning activities and military operations
4. Real world considerations
• we start from a brownfield site
• need to straddle multiple branches
• data is everywhere but what matters most?
• there is no intelligence but information of specific value
• essential enabling conditions & foundations?
• we are we are still talking about the chaos of war
• our enemies have a very real vote
• our ability to operate over degraded networks and
federated command and control
5. Mission threads
• look beyond information exchange
requirements (IERs)
• gaps in our staff process/approach
• information must be treated and
consumed as an essential service
• must be command-driven and
anticipatory
6. Human information interaction
OFFICIAL
How can I (and my team):
• rapidly and intuitively locate key information for my role
• indicate that certain information is important, and why and when so I
can find it again
• record/create information without worrying where it is located and
not being able to find it again
• record key relationships between information
• understand accuracy and provenance
• be told if I need to know but don’t have permission to access
• prevent being swamped by the scale and complexity of available
information
7. Wider Defence Lines Of Development
(DLOD) considerations
• personnel – what key skills and experience do we develop?
• doctrine – can we conceptually keep pace?
• infrastructure - What is the technology readiness level (TRL)
‘aiming point’?
• training
• individual, professional and collective burden?
• TRAIN AS WE FIGHT
• interoperability – designed in at the outset
9. OFFICIAL
Aims
OFFICIAL
Obtain and exploit innovative ideas that:
• Ensure that human cognitive capacity (which is limited) is applied to
those parts of military problems that humans can undertake best
• Reduce unnecessary consumption of human cognitive capacity on
activities better supported by automation
• Achieve the above by ensuring that human and automated parts
work effectively in unison avoiding pitfalls and problems
14. OFFICIALOFFICIAL
Reasoning
Record and process reasoning related information
• represent/store questions, hypotheses, assumptions and
uncertainties
• continuously check reasoning against incoming data stream
• apply reasoning to generate new findings, create new
questions and hypotheses etc.
15. OFFICIALOFFICIAL
Relevant roles
Illustration by Andrew Rae
Tendency to automate everything or roles which humans can
do better
• for example abstraction, pattern matching across diverse input, self
assessment/reflection, idiosyncrasy, creativeness
Interested in
• novel approaches which demonstrate more appropriate assignment
of relevant tasks/roles to human and machine
• approaches which keep human interested, engaged and workload at
appropriate level (no under/overload)
Overall Concept
• team design based on SQEP of human and machine parts
16. OFFICIALOFFICIAL
Individual and team interaction
Tendency to stove-pipe human machine tasks/roles
• no effective team-working between human and machine
• teaming ‘capacity/behaviours’ is difficult
Interested in solutions that
• improve interworking based on a equivalent team member interaction
concept
• exploit team contextual information
• dynamically vary what human/machine parts are doing
• Overall concept
• augment human teams with machine team members
17. OFFICIALOFFICIAL
Summary
We are interested in solutions:
• which take account of team context
• that don’t increase training load, are intuitive to use, and adoptable by
non-experts operating in stressful environments
• that can start small and simple, have rapid application, but have the
potential to scale up
We are not interested in solutions:
• that replace the human component or relegate role of the human
• which fail to take account of identified automation pitfalls
• which might force people into unnatural ways of operating
• that are stand-alone human machine interaction technologies
18. Joint Warfare DirectorateJoint Warfare Directorate
Summary
• challenges applicable across Defence
• competition split into 3 challenges but solutions will
sit across the challenges
• interested in how we win through adapting our
processes, people and technology
• trying to ensure we don’t fight a fair fight
Hinweis der Redaktion
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Hi, my name is Peter Houghton and I am a Dstl scientist specialising in the area of military command and control systems. I would now like to brief you on Challenge 3 which is concerned with trying to make more effective use of limited human cognitive capacity, specifically the capacity that is put under significant stress in the challenging conditions that military staff find themselves in.
So focusing on the overall aims of this particular challenge, we are interested in ideas that you might have which can focus human cognition on those parts of military problems which we understand are best undertaken by people; in ideas that can prevent precious capacity being wasted on activities which we believe machines are more suited to, and most importantly for this challenge, to achieve these things by improving the interaction between human and automated parts. There are many well known pitfalls in doing this and many myths surrounding what automation can do, and we will be looking for solutions which clearly and demonstrably avoid these.
So what the limitations that we are concerned with? There are many which could be listed, and the following are just a few examples of those relevant to the way that people interact with information.
First - there are only so many things that we can attend to at any one point in time, despite those who believe that they are effective multi-taskers (an example of one of those myths ). Most will be aware of the magic number 7 and that short term memory only exists for around 15-30 seconds. However, we don’t just have a problem with storage, we also have problems with recall, particularly for those like myself who are now of advancing years .
People also find it difficult to spot patterns spread over time, as each point in the pattern is so disconnected from the others. We also suffer from what can be called a “law of cognitive effort” which if you read Daniel Kahneman’s book, suggests may be the physiological factor of thinking consuming considerable amounts of energy, and hence we have a natural tendency to minimise it. Kahneman also describes other cognitive tendencies, such as becoming trapped in certain trains of thought due to sunk costs, and that our intuitive thought is often at odds with the way that probability and statistics actually work.
Short term memory has three key aspects:
1. limited capacity (only about 7 items can be stored at a time)
2. limited duration (storage is very fragile and information can be lost with distraction or passage of time)
3. encoding (primarily verbal/acoustic).
It’s not just humans which have limitations, automated systems have them too. I realise that more recent advances probably mean that some of these are being nibbled away at, but I would be genuinely surprised if any of you can demonstrate that you have cracked these in anything other than very constrained problem domains, or that your solutions can safely evolve themselves without considerable specialist human attention being applied. Note that the latter would limit the applicability of such automation in many areas of defence as situations are very diverse, dynamic and unpredictable.
I’ll now move on to focus on our areas of interest for any solutions that you might have, be they at the research stage or nearer-term application stage of development. I’ll also bring in the key part of this challenge which is concerned with human machine teaming.
So given the human limitations that I talked about a moment ago, can we help people record and recall important information - both across minute-to-minute timescales and across the longer-term and under varying workload and diverse environmental settings?
We are interested in solutions to aid rapid and intuitive recording and retrieval of discoveries, facts, contacts, reminders, progress/status, multi-media and ….also the ability quickly and easily record relationships between these things or have the automation help in the discovery of them.
The overall concept here is one of augmented human memory (so by combining human and machine parts – we have more effective storage and retrieval, and that includes exploiting the powerful associative part of human memory that enables us to locate related information too).
It’s not just memory that can in principle be aided by automation, it could also help us with our reasoning. Here, instead of just recording “facts”, (avoiding the alternative ones ), automated systems could help us record and process reasoning related information, including questions, hypotheses, assumptions and uncertainties. Because a machine doesn’t get bored, or lose attention, and can undertake its reasoning at speed and scale, it could help us by continuously checking our reasoning against incoming data streams and also apply it’s own generic reasoning to generate new findings, or create new questions and hypotheses etc.
Focussing again on the teaming aspect, we are particularly interested in systems which can explain their reasoning in a human intuitive manner (in support of human sensemaking).
Thus the overall concept here is one of providing machine augmentation to human reasoning, pattern finding and matching.
If one looks at the history of automation, there has been a distinct tendency to automate everything, including activities where presently humans might perform better – examples would be abstraction, pattern matching across diverse inputs, self assessment and reflection, or activities where idiosyncrasy, and creativeness are important. If I were to try and summarise when and why these are important; they are those which may be critical for dealing with complex, uncertain and unpredictable situations.
We are therefore interested in novel approaches that demonstrate more appropriate assignment of relevant tasks and roles to the human and machine components, and where human needs are better accounted for i.e. those that keep the human interested, engaged and their workload at an appropriate level (i.e. with no under or overload).
Thus the overall concept here is designing a human/machine team, where the design is based on a much more comprehensive understanding of the different strengths and weaknesses of the human and machine parts.
Even if we do get the role and task assignment correct, we can then fall into another trap; that of tending to stove-pipe the human and machine parts. The end result is that we don’t have effective team-working between the human and the machine parts. It’s perhaps easy to see why this might be the case, as understanding teamwork supporting behaviours, such as being able to sense when your colleagues are stressed or overworked, or when they are stopping and starting new activities, can be very difficult for machines to do.
We are therefore interested in solutions which could improve interworking between people and machines based on the idea of the machine being a more realistic, equivalent team member; one that can detect and exploit team contextual information, and use this to vary what the machine and human parts are doing. An example of such an idea is described in the literature as “affective computing”, (book by Rosalind Picard) which suggests that the machine should try and interpret the emotional state of humans and adapt its behaviour to them.
Thus, the overall concept here is one of augmenting human teams with machine team members.
So to summarise – we are interested in solutions that:
can operate more like a supportive team member which means taking account of team context
aren’t going to create a new training load due to complexity and can be operated in environments with high stress and workload
can start at small scale, can be rapidly applied but have the potential to scale up to more sizeable and more capable aids in the longer term
We are not interested in solutions that:
completely replace the human or relegate the role of the human to mere supervisor or integrator of the left over parts
fail to take account of known pitfalls with automating human activities which are well documented in the literature
which could result in people having to accommodate the technology by working or behaving in unnatural ways
comprise of interaction technologies on their own – they must be integrated into a larger information interaction and processing capability
Thank you for your attention, that concludes my talk.
