The document discusses applying the Drools rules engine in assistive technology, noting that Drools could help address challenges in developing highly personalized and adaptive assistive devices by allowing rules to be easily changed without requiring full system redeployment. It provides an example of how fuzzy logic has been used to improve mouse cursor control for motor-impaired individuals and suggests Drools may help develop more sophisticated assistive technology systems through its event processing capabilities.

2. Applying JBoss Drools in Assistive Technology

3. Who is Tihomir? R&D Engineer on Drools

4. Contributor and committer on Seam

5. Agenda Assistive Technology (AT)

6. Applying Drools in AT

7. Event Processing

8. Drools Fusion examples

9. Vision for the future

10. Q&A

11. Assistive Technology Disability affects 15-20% of every country's population (with 10% being classified with severe motor impairments). Growing trends (aging population, severe stroke).

12. Categories of disabilities: - Sensoric deficiencies – reduced ability to acquire inputs from the physical world (e.g. blindness, deafness, etc.), - Expression deficiencies – reduced ability to produce output to the physical world (e.g. voice, motion, etc.). Assistive technology (AT) – product, device or equipment (e.g. assistive, adaptive or rehabilitative devices) that helps individuals with disabilities to increase, maintain or improve functional capabilities (AT Act, 1998).

13. AT products help peoples with disabilities to achieve greater independence, enhance their quality of life and compete in the real world ( inclusive society ).

14. AT Current situation Steadily growing market (especially for small businesses, estimation: US$ 12 billion in USA, 30 billion in Japan and 40 billion in Europe)

15. Modern AT industry sustainable and profitable.

16. The key problems: - User View – Expensive, not sufficiently flexible and adaptable devices. Slow and not responsive systems, non-transparent, difficult to use, sensitive to disturbances and errors. - Developer View – Demands for highly customized products within a huge target-audience, risky business (user acceptance, competitive pricing).

17. AT research and development Long term AT research (priorities in several countries).

18. Main focus: respond to the needs and wants of users.

19. Close relationships to main-stream researches, such as robotics, cognition, AI, IT, sensor technology, real time control etc.

20. Attractive common research focuses on: - human enhancement, - rehabilitation robotics, - human-robot interaction (HRI), - human-computer interfaces (HCI), - brain-computer interfaces (BCI), - computer vision, etc.

21. AT essential distinctive problem Extreme individual differences that characterize abilities and needs of motor-impaired people.

22. Keys to increase acceptance and use High personalization and customization

23. Scalability and adaptivity

24. Improve performance over time

25. Acquisition/preservation of user experience and knowledge

26. Improve maintainability and robustness in spite of user and environmental disturbances

27. AT example: computer access Computer input devices for motor-impaired people

28. Mouse cursor control for handicapped individuals (MCC) – main operations: pointing and clicking .

29. Ca. 250 commercial MCC products available at the market worldwide.

30. Multitude of MCC concepts: switch-related systems (sip/puff switches), head pointing systems, eye tracking systems, BCI etc.

31. None of them provide a standardized general system for subjects with severe disabilities.

32. MCC AT principles

33. Roadmap to success More complex systems that combine several input channels.

34. Use of innovative technologies, such as AI approaches, methods and tools (e.g. automated reasoning, first-order logic, fuzzy logic, neural-networks etc.).

35. Development of standardized HW and SW modules that can be configured for a specific user.

36. Example: Improve MCC by using fuzzy logic Convenient simple input device: pneumatic dual-action input switch (sip and puff).

37. Indirect pointing of target items (scanning) based on Morse codes sequences of dots (sip) and dashes (puff).

38. Traditional line scanning.

39. Quite inefficient and tedious, especially for users with limited breathing or learning capabilities.

40. Why Fuzzy Logic? Dealing with imperfect information natively IF direction IS positive AND strength IS weak THEN output IS weak_positive

41. Fuzzy scanning and inference algorithms Puff/sip define cursor motion directions.

42. New input air flow defines velocity of cursor moving.

43. Fuzzy scanning algorithms: circular scan, selection area scan, object selection, etc

44. Fuzzy control outcomes and limitations Improved mouse control. Clear step-forward in comparison to the state of art.

45. Feasible and promising solution for development of sophisticated and adaptive MMC (HCI) systems.

46. Constraints: relatively good breathing control is needed. Sensitivity to disturbances.

47. Multi-channel inputs and action outputs (MIMO) can further improve performance and robustness, however fuzzy logic and control applications become extremely complex.

48. Processing large number of input events becomes problematic.

49. Beyond just fuzzy reasoning Needs: production rule system

50. ability to combine classical predicate logic with fuzzy logic to represent the system as combination of discrete as well as continuous events.

51. ability to process large number of complex events through multiple input channels.

52. BPM capabilities

53. tooling and testing support

54. ability to embed (parts of) system One-stop-shop solution??

55. Drools (1) “ A common platform to model and govern the business logic of the enterprise.”

56. Drools (2) “ A common platform to model and govern the business logic of the enterprise.”

57. Drools for AT? (1) Fully integrated logic platform (rule engine, workflow, event processing, etc) that supports easy and frequent change of rules (adaptation to users).

58. Extensions of the RETE networks using set of pluggable modules.

59. Drools-Chance (still in development) makes Drools capable of dealing with imperfect information natively. Also adds possibility to configure behavior of the engine to reason with different type of imperfection without changing the production rules.

60. Tooling support

61. Ability to test/monitor all parts of AT system.

62. Ability to run/embed into OSGi environments and future AT devices.

63. Drools for AT? (2) Separates applications (AT devices or computer control systems) from conditions which control the application flow.

64. Changing rules does not require a complete deployment of AT systems.

65. Provides background to build modular and scalable AT systems, such as integrate additional sensors for a specific user and develop rules, without influencing specific application (e.g. computer or assistive device control).

66. Rules can be modified by different group of users (technicians, physicians, end-users).

67. Rule language is simple to learn.

68. End-users can be involved to resolve conflicts and perform specific actions (improved robustness).

69. Efficient for frequent change of rules dependent on user experience, health state, specific needs etc.

70. First steps toward improvement for MCC - Event Processing Any form of computing performing operations on events

71. Event: Real world: occurrence of something that has happened at a particular point in time

72. Comp. Systems: entity/object that represents this occurrence

73. 1 RW Event -> ∞ CS Events

74. Raw vs. Derived Events Complex Event: abstraction of a number of related events For example a hurricane is an abstraction of a large number of natural eve nts

75. Main Concepts of Event Processing Event Processing Networks (EPN): Producers: introduce/emits events into the processing network (Sensors, Systems, Apps, Business Processes, Human Interaction, etc)

76. Distribution Channels: provides means of distributing events (typically one-to-many distribution, async)

77. Consumers: receive and act upon events (Applications, Systems, Data Stores, Business Processes, etc)

78. Intermediary Processing: rout / filter (raw events), produce (derived events). Consists of a number of building blocks (Agents) Intermediary Processing Event Producers Event Consumers

79. Event Processing Agents Building Blocks for: Translation, Composition, Aggregation, Enrichment, Splitting, Projection, Pattern Detection, etc. Even Filtering Event Matching Event Derivation time time time

80. Event Processing Network (1) Network based on the core EP building blocks

81. Can be described as process flows such as Drools-Flow

82. Event Processing Network (2) Describing network building blocks with Drools-Flow bring good feature support to EP Custom work item handlers

83. Testing, debugging, monitoring (and self-monitoring/adapting)

84. Customizable and easily replaceable network parts

85. Integration with Human tasks public class ErrorEventListener implements ProcessEventListener { public void afterNodeLeft(ProcessNodeLeftEvent event) { ksession .getWorkingMemoryEntryPoint( entryPointName ).insert(event); ... } declare ProcessNodeLeftEvent @role( event ) end rule "Number of Cursor Rotations is above threshold" when Number( nbProcesses : intValue > 5 ) from accumulate ( e: ProcessNodeLeftEvent( nodeInstance.nodeName == "CursorRotationNode" ) over window:size(10s), count(e) ) then drools.getWorkingMemory().getProcessInstance(ATDepartmentNotificationProcess).signalEvent( "notify" , "cursor rotation threshold exceeded" ); end

86. Event Processing - Context Specifies conditions on a group of events so they can be processed in related ways.

87. Events may/may not be relevant in certain contexts Example: Left-click command can only be issued over clickable objects on screen Types of contexts: Temporal: consists of time intervals and events within intervals

88. Spacial: takes account geospatial event info

89. State-oriented: depends on state of some external entity

90. Segmentation-oriented: partitions events by their attribute values (for example age >= 35)

91. Drools Fusion

92. Drools Fusion – Events (1) Representing Raw Events:

93. Creating Derived Events: declare Direction @role( event ) @timestamp( timestamp ) @expires( 1m ) end declare Strength @role( event ) @expires( 1m ) end declare DoubleClick @role( event ) direction : Direction strength : Strength delay : int end rule detectDoubleClick when $direction : Direction( ) from entry-point "Raw-Direction-Stream" ... $strength : Strength( ) from entry-point "Raw-Strength-Stream" ... then DoubleClick dc = new DoubleClick($direction, $strength, 0); entryPoints[ "Cursor-Control-Endpoint-Stream" ). insert (dc); end

94. Drools Fusion – Events (2) Absence of events in proper order:

95. Negative patterns, waiting for events to happen: declare Click @role( event ) @expires( 1m ) delay : int end declare Drag @role( event ) @expires( 1m ) direction : Direction strength : Strength delay : int end rule invalidDragMotion when $dragEvent : Drag( ) from entry-point "Cursor-Control-Endpoint-Stream" not ( Click( this before $dragEvent ) from entry-point "Cursor-Control-Endpoint-Stream" ) // click starts drag event then # notify user+system of invalid command ... end rule lenientDragMotion when $dragEvent : Drag( ) from entry-point "Cursor-Control-Endpoint-Stream" not ( Click( this before $dragEvent ) from entry-point "Cursor-Control-Endpoint-Stream" ) Click( this after[0s,5s] $dragEvent ) from entry-point "Cursor-Control-Endpoint-Stream" then # allow drag motion... ... end

96. Drools Fusion – Event Streams Drools compiler gathers all entry-point declarations and exposes them through the session API.

97. Engine manages all scoping and synchronizations behind the scenes.

98. Event processing from specific streams in rules: StatefulKnowledgeSession ksession = ... ... WorkingMemoryEntryPoint rawDirectionEntryPoint = ksession.getWorkingMemoryEntryPoint( "Raw-Direction-Stream" ); WorkingMemoryEntryPoint rawStrengthEntryPoint = ksession.getWorkingMemoryEntryPoint( "Raw-Strength-Stream" ); … // Every time we get input from direction sensor do: rawDirectionEntryPoint.insert(direction); // Every time we get input from flow sensor do: rawStrengthEntryPoint.insert(strength); ... rule detectVerticalScrolling When ... $direction : Direction( ) from entry-point "Raw-Direction-Stream" $strength : Strength( ) from entry-point "Raw-Strength-Stream" ... then ...

99. Drools Fusion - Channels Eliminates runtime awareness of event consumers.

100. Dynamic registration of consumers in channels possible.

101. Example: a) Register Channel:

102. b) Channel Implementation:

103. Push derived events for distribution in rules: StatefulKnowledgeSession ksession = kbase.newStatefulKnowledgeSession(config, null ); ksession.registerChannel( &quot;cursor-control&quot; , new CursorControlChannel()); ... public class CursorControlChannel implements Channel { private List<MouseControlService> controlServices ; @Override public void send(Object command) { for (MouseControlService service : controlServices ) { service.publishCommand(command); } } ... rule detectDoubleClick when ... then DoubleClick dc = new DoubleClick(); ... channels[ &quot;cursor-control&quot; ].send( dc ); end

104. Drools Fusion – Temporal Event Context (1) Enable Temporal Context by putting engine in “Stream mode”:

105. Choose a Clock type (realtime or pseudo for testing): ... KnowledgeBaseConfiguration config = KnowledgeBaseFactory. newKnowledgeBaseConfiguration (); config.setOption( EventProcessingOption. STREAM ); KnowledgeBase kbase = KnowledgeBaseFactory. newKnowledgeBase (config); ... ... KnowledgeSessionConfiguration config = KnowledgeBaseFactory. newKnowledgeSessionConfiguration (); config.setOption( ClockTypeOption. get ( &quot;realtime&quot; ) ); StatefulKnowledgeSession ksession = kbase.newStatefulKnowledgeSession(config, null ); ...

106. Drools Fusion – Temporal Event Context (2) Stream Mode Enables: Notion of “flow of time”, concept of “now”

107. Automatic event lifecycle management

108. Support for sliding windows

109. Automatic rule delaying on absence of facts Reference Clock (Session Clock) implementations:

110. Drools Fusion – Temporal Event Context (3) Sliding Window support allows reasoning over a window of interest: Time

111. Length rule &quot;Normalize cursor motion speed&quot; when CursorSpeedThreshold( $max : max ) Number( doubleValue > $max) from accumulate ( Strength( $val : value ) over window:length( 200 ), average( $val ) ) then // normalize the cursor speed end rule “Detect excessive Cursor Rotation” when Number( intValue > 15 ) from accumulate ( CursorRotation( ) over window:time(30s), count(e) ) then drools.getWorkingMemory().getProcessInstance(ATDepartmentNotificationProcess).signalEvent( &quot;notify&quot; , &quot;cursor rotation threshold exceeded&quot; ); end

112. Drools Fusion – Temporal Context Operators 13 Temporal Operators

113. Drools Fusion – Temporal Context Operators

114. What next? -AT vision architecture

115. Questions? THANKS!

116. Project: http://www.jboss.org/drools

117. Reference Manual: http://www.jboss.org/drools/documentation.html

118. Blog: http://blog.athico.com/

119. Mailing Lists: http://www.jboss.org/drools/lists.html