Peter sawyer caise

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Peter sawyer caise

  1. 1. Modeling Pesonalized Adaptive Systems Alistair Sutcliffe and Pete Sawyer University of Manchester and Lancaster University
  2. 2. Motivation •  Traditionally: –  Systems developed for an idealized/homogenized user or categories of users •  Possibly with some tailoring •  Increasingly: –  Systems need to be tailored to individual users and their contexts and to adapt as users’ needs evolve –  Recent trends towards self-adaptive systems reflect this •  Our aim: –  A model-based framework for personal and contextual modeling
  3. 3. Personal Contextual Knowledge People have … …. information describing them … and information held by them Some of this information changes over time and according to context. Is a journalist Values privacy
  4. 4. General stakeholder requirements Spacial change Business and domain evolution, expert users, .. Culture and localisation, interaction language, … User characteristics requirements Individual user skill and ability, … Physical context, social context, … Personal goals Attain individual goals Location, social context A two-layer framework
  5. 5. User Characteristics •  The information describing someone •  Includes their skills and knowledge but also cognitive, physical and perceptual abilities. –  e.g. Truck driver has a commercial driving license (skill) but also myopia (perceptual ability) •  May change with time –  Myopia may get worse, spacial orientation skills may decline as driver ages •  May change with context –  Driving around home town vs. driving around an unfamiliar city, reactions and spacial awareness when fresh vs. when tired
  6. 6. User Characteristics (contd.) •  Where useful? –  Depends on application (of course), e.g.: •  Socio-technical systems involving physical action, or action in virtual environments that might lack physical cues •  Users of assistive technologies in which each user will need care tailored to their condition(s) –  Systems tailored to skills and abilities, but also adapt as these change over time e.g.: •  Skills improve •  User’s condition progresses
  7. 7. Personal Goals •  The information held by someone, but also their attitudes and preferences •  Values are key components of personal goal attainment.
  8. 8. Personal Goals (contd.) Relations with others “Big five”
  9. 9. Personal Goals (cont.d) •  The information held by someone, but also their attitudes and preferences •  Values are key components of personal goal attainment. •  Values tend to be less time-variant than personal characteristics •  Values are a diverse set of properties but their usefulness lies in being able to understand their impact on personal goal attainment, e.g.: –  concerns about privacy is something social network systems have to deal with
  10. 10. The Framework: broad principles •  User characteristics used as a checklist to identify potential obstacles to system goal satisfaction •  In the personal goals layer, values may represent “weak obstacles”; probabilities that user behaviour will inhibit attainment of system goals
  11. 11. Case Study •  An Ambient Assisted Living System •  Two users –  Mary –  Mary’s carer •  Mary has: –  Limited mobility •  Able to live at home with assistance –  Mild Cognitive Impairment •  May forget things
  12. 12. AAL KAOS Goal Model 1 Achieve[Release Dose] Achieve[ MedicineTaken] Maintain[Is Healthy] Achieve[Correct MedicineDose] Dispenser   Mary   Top level system goal An expectation Human agent System agent A requirement
  13. 13. Personal Characteristics •  Mary’s key characteristic is her MCI –  She might forget to take her medicine •  Leads to the obstacle Forgets to Take Medicine •  Mary’s MCI will probably get worse over time
  14. 14. Achieve[Release Dose] Achieve[ MedicineTaken] Maintain[Is Healthy] Achieve[Correct MedicineDose] Dispenser   Mary   Forgets to take medicine Underdose Overdose Top-level obstacle AAL KAOS Goal Model 2 Threatens
  15. 15. AAL KAOS Goal Model 3 Achieve[Release Dose] Achieve[ MedicineTaken] Maintain[Is Healthy] Achieve[Correct MedicineDose] Dispenser   Mary   Forgets to take medicine Underdose Overdose Achieve[Prompt ToTakeMedicine] Achieve[Remind MedicineUntaken] Maintain[Monitor DispenserTray] AAL   AAL   Achieve[Detect UntakenMedicine] Mitigates Mary wants to maintain her health Domain assumption
  16. 16. So far … •  We’ve used one of Mary’s personal characteristics to guide obstacle analysis for the system goal model •  Now we will analyse Mary’s personal goals to investigate whether there are any further (“weak”) obstacles that arise from Mary’s values and that need to be mitigated. •  We use Mary’s values to help understand softgoals; system qualities •  We start by eliciting these system qualities and modeling them as softgoals
  17. 17. AAL KAOS Goal Model 4 Achieve[Release Dose] Achieve[ MedicineTaken] Forgets to take medicine Underdose Maintain[Is Healthy] Achieve[Correct MedicineDose] Overdose Dispenser   Achieve[Prompt ToTakeMedicine] Achieve[Remind MedicineUntaken] Maintain[Monitor DispenserTray] Mary   AAL   AAL   Achieve[Detect UntakenMedicine] Mary wants to maintain her health + - Avoid intervention Minimize intrusion
  18. 18. Effect of Mary’s values on Minimize Intrusion quality Minimize intrusion frustration resentment emotional resonse cooperation openness + + = + = Mary is open and cooperative. This potentially gives hare a neutral attitude to intrusive interventions by the AAL
  19. 19. Effect of Mary’s values on Minimize Intrusion quality Minimize intrusion frustration resentment emotional resonse cooperation openness + + = + = However, Mary suffers feelings of frustration at her condition and may feel resentment that interventions are needed. This strengthens her intent to avoid interventions but may lead her to ignore or attempt to subvert reminders This should lead us to mitigate the risks – perhaps by careful design of the reminders – e.g. to make them empathic
  20. 20. Uncertainty 1 •  Mary’s values may be estimated in a number of ways: –  Online tests –  Estimates from her carer –  Estimates from domain experts •  Clearly there will be significant uncertainty about the nature and strength of her values
  21. 21. Uncertainty 2 Minimize intrusion frustration resentment emotional response cooperation openness + + = + = •  Is this the right set of values for Mary? •  Do they have the same or different relative weights? •  Are the propagated values (the ‘+’, etc.) the right ones? •  These are probably best understood probabilistically •  Baysian reasoning may be of help here.
  22. 22. Time and Context •  So far we have not considered time or context •  But these might be relevant, e.g.: –  Mary’s condition might progress and reminders might have to become more frequent –  There may be contexts in which Mary more commonly forgets to take her medicine •  Monitoring (A key component of an adaptive system) could be used to discover these. •  The framework should help us identify what to monitor
  23. 23. Next Steps •  We are applying the framework to the EPSRC SAMS project1 –  Software Architecture for Mental health Self management •  SAMS aims to encourage self-referral for people with the early signs of dementia •  SAMS will apply text and data mining techniques to look for signs of Mild Cognitive Impairment by passive monitoring of peoples’ interaction with their computer 1EPSRC grant EP/K015796/1
  24. 24. Conclusions •  We are interested in how to systematize the design of systems that have a focus on the individual user •  We propose a two-layer framework based on well- developed models of personal characteristics and personal goals •  We use these to help us identify strong and weak obstacles to attainment of system and user goals, using goal modeling. •  Still to do: –  Validate the sets of characteristics and values –  Develop means of reasoning about their effects on goal attainment

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