Knowledge engineering tools and techniques for AI planning

Preface......Page 5
Contents......Page 7
Part I Knowledge Capture and Encoding......Page 9
  1 Introduction......Page 10
   2.1 Classical Planning with Conditional Effects......Page 11
   2.2 The Observation Model......Page 12
   2.3 Explaining Observations with Classical Planning......Page 13
   3.1 The Space of Strips Action Models......Page 14
   3.2 The Sampling Space......Page 16
   4.1 Compilation......Page 17
   4.2 Properties of the Compilation......Page 22
  5 Experimental Results......Page 23
   5.2 Learning from Initial/Final State Pairs......Page 24
  6 Conclusions......Page 25
  References......Page 26
  1 Introduction......Page 28
   1.1 Knowledge Representation for Knowledge Engineering of Domain Models......Page 30
  2 Domain Model Learning Techniques and Tools......Page 31
   2.1 Inductive Learning......Page 32
    2.1.1 When to Use Inductive Learning......Page 34
   2.2 Knowledge-Based Inductive Learning (KBIL)......Page 35
   2.3 Analytical Learning......Page 38
   2.4 Hybrid Learning......Page 39
   2.5 Surprise-Based Learning (SBL)......Page 40
   2.6 Transfer Learning......Page 42
   2.7 Policy Learning......Page 43
   2.8 Other Methods of Knowledge Acquisition......Page 44
  3 Characteristics of the Domain Model Learning Tools......Page 46
  4 Conclusion......Page 49
  References......Page 50
 3 Formal Knowledge Engineering for Planning: Pre and Post-Design Analysis......Page 54
  1 Introduction......Page 55
  2 Knowledge Engineering and Planning......Page 56
  3 Domain Modeling in AI Planning......Page 58
   3.1 Accuracy......Page 59
   3.2 Adequacy......Page 60
   3.3 Operationality......Page 61
  4 A Knowledge Engineering Design Approach for Planning......Page 63
  5 PDM and Post-Design Modeling Using Petri Nets......Page 66
  6 New Perspectives for AI Planning in Automation Systems......Page 69
  References......Page 70
  1 Introduction......Page 73
  2 Related Work......Page 75
   2.1 Critical Review......Page 78
   3.1 Modules......Page 80
   4.1 User Evaluation......Page 86
    4.1.2 Results......Page 88
  5 Conclusion......Page 91
   Deliberately Erroneous Logistics Domain......Page 92
   Deliberately Erroneous Coffee Domain......Page 93
   Planet Splisus......Page 94
  References......Page 95
 5 KEPS Book: Planning.Domains......Page 97
  1 Planning.Domains Solver......Page 98
   1.1 Libraries......Page 99
   1.2 API Future......Page 101
  2 Solver Planning Domains......Page 102
  3 Editor Planning Domains......Page 103
   3.1 Plugin Framework......Page 104
   3.3 Editor Future......Page 105
   5.1 Planimation......Page 106
   5.2 VSCode Integration......Page 109
  References......Page 110
  1 Introduction......Page 112
  2 Outer Entanglements......Page 113
  3 Macro-Operators......Page 115
  4 Bagged Representation......Page 118
  5 Procedural Domain Control Knowledge......Page 120
  6 Transition-Based Domain Control Knowledge......Page 121
  7 A Case Study: The Spanner Domain......Page 124
  8 Conclusion......Page 125
  References......Page 126
Part II Interaction, Visualisation, and Explanation......Page 129
  1 Introduction......Page 130
   2.1 The Planning Problem......Page 133
   2.2 The LPG Planner......Page 136
    2.2.1 Plan Representation Through LA-Graphs......Page 137
    2.2.2 Local Search in the Space of LA-Graphs......Page 138
   3.1 Architecture Overview......Page 139
   3.3 Search Process Monitor......Page 142
   3.4 Search State Monitor......Page 143
   3.5 Plan Editor......Page 145
   3.6 Search Process Editor......Page 146
  4 Walk-through Example of a User Interaction......Page 147
  5 Experiments......Page 152
  6 Related Work......Page 154
  7 Conclusions......Page 155
  References......Page 156
  1 Introduction......Page 159
   2.1 Problem Specification......Page 160
   2.2 Visualization of Schedules......Page 161
   2.4 Automated Schedule Repair......Page 162
  3 Interactive Workflow Optimization (FlowOpt)......Page 165
  4 Interactive Visualization and Verification of Plan (VisPlan)......Page 167
   4.1 Plan Verification......Page 168
   4.2 Visualization of Sequential and Temporal Plans......Page 169
    4.2.2 Visualization of Temporal Plans......Page 171
   4.3 Interactive Plan Modifications......Page 172
  References......Page 173
  1 Introduction......Page 175
  2 Argumentation and Dialogue......Page 176
   2.1 Abstract Argumentation......Page 177
   2.3 From Knowledge to Arguments......Page 178
  3 Proof Dialogues......Page 181
  4 Putting it all Together: The SAsSy Demonstrator......Page 184
   4.2 Natural Language Generation......Page 185
   4.3 Dialogue Based Plan Explanation......Page 186
  5 Discussion and Related Work......Page 187
  References......Page 188
 10 Interactive Planning-Based Hypothesis Generation with LTS++......Page 191
  1 Introduction and Motivation......Page 192
  2 Application Description......Page 193
  3 Hypothesis Generation Problem......Page 196
  4 Model Description in LTS++......Page 199
   4.1 From LTS++ to a Planning Problem in PDDL......Page 201
  5 LTS++ Integrated Development Environment......Page 204
  7 Summary......Page 207
  References......Page 208
  1 Introduction......Page 210
   2.2 Data Visualization......Page 212
  3 Web Planner Architecture......Page 213
   3.1 Domain Development Interface......Page 214
   3.2 Visualization Interface......Page 216
   4.1 Case Study......Page 220
   4.2 Case Study Survey Results......Page 222
  5 Related Work......Page 223
  6 Conclusions......Page 226
  References......Page 227
Part III Case Studies and Applications......Page 229
  1 Introduction......Page 230
   2.1 A Theoretical Framework......Page 232
   2.2 PLATINUm: A Timeline-Based Planning and Acting Framework......Page 234
  3 KeeN: Knowledge Engineering ENvironment......Page 235
   3.1 Knowledge Engineering and Verification and Validation Features in KeeN......Page 236
  4 Deploying Task Planning Solutions for Safe Human-Robot Collaboration......Page 238
   4.1 A Specific Human-Robot Collaboration Case Study......Page 239
   4.2 An Engineering and Control Architecture for HRC......Page 240
   4.3 The FourByThree Controller......Page 242
   4.4 Implementation with a Real Robot......Page 244
  5 Conclusions......Page 245
  References......Page 246
  1 Introduction......Page 248
  2 Background......Page 249
   3.1 Requirements......Page 250
   3.2 Domain Model Specification......Page 251
   3.3 Problem Specification......Page 252
  4 Dynamic Planning, Replanning, and Plan Execution......Page 253
   4.2 Domain Model Specification......Page 254
   4.5 Field Experiment......Page 256
  References......Page 257
  1 Introduction......Page 259
  2 Interaction Management......Page 261
  3 Task-Based Social Interaction: A Robot Bartender Scenario......Page 262
   4.1 Planning with Knowledge and Sensing......Page 263
   4.2 State Management......Page 265
   4.3 Representing Properties, Actions, Objects, and Goals......Page 266
   5.1 Ordering a Drink......Page 268
   5.2 Ordering Drinks with Multiple Agents......Page 269
   5.3 Ordering a Drink with Restricted Drink Choices......Page 270
  6 Plan Execution, Monitoring, and Recovery......Page 271
  7 Discussion and Conclusions......Page 273
  References......Page 274