About this Research Topic
Goal and Plan recognition are two interdisciplinary problems that can be addressed using techniques from automated planning, natural language understanding, psychology, human-computer interfaces, machine learning, and more. However, many of these techniques cannot be trivially applied for goal and plan recognition. For example, when leveraging automated planning for goal recognition, additional information may be required for the recognition task, with respect to what is needed for compared to the planning task. This can include various limitations on the observations such as partialness and noise, correctness, partial domain model, and missing knowledge of an agent's preferences. As AI systems become more prevalent, new challenges arise to create accurate, explainable, and robust methods for goal and plan recognition algorithms in the real world.
Goal and plan recognition research have seen substantial recent research activity. Efforts include relaxing virtually some assumptions about the underlying recognition problems, dealing with domain models with imperfections and spurious observations, expanding recognition to continuous and stochastic domains, as well as those populated by multiple agents.
We are interested in original research and survey articles on the following, non-exhaustive list of topics:
● Goal/Plan recognition approaches using any type of domain model
● Domain theory formalisms for goal and plan recognition
● Learning domain models for goal and plan recognition
● Recognition techniques to cope with domain imperfections (incomplete or faulty domain models, approximate domain models, and etc.)
● Integration of classical planning and learning for goal and plan recognition
● Explainable goal and plan recognition
● Applications of goal and plan recognition (services for helping the elderly people, identifying significant activities and places from GPS traces, parsing algorithms, Bayesian networks inference procedures and goal recognition for dialogue systems among others)
● Multiagent goal and plan recognition
● Domain design for goal and plan recognition
● Recognition of goals and plans of humans vs. virtual agents.
● Goal acquisition/elicitation for goal/plan recognition
● Activity recognition in isolation or integrated with Goal and Plan Recognition
Goal and plan recognition research have seen substantial recent research activity. Efforts include relaxing virtually some assumptions about the underlying recognition problems, dealing with domain models with imperfections and spurious observations, expanding recognition to continuous and stochastic domains, as well as those populated by multiple agents.
We are interested in original research and survey articles on the following, non-exhaustive list of topics:
● Goal/Plan recognition approaches using any type of domain model
● Domain theory formalisms for goal and plan recognition
● Learning domain models for goal and plan recognition
● Recognition techniques to cope with domain imperfections (incomplete or faulty domain models, approximate domain models, and etc.)
● Integration of classical planning and learning for goal and plan recognition
● Explainable goal and plan recognition
● Applications of goal and plan recognition (services for helping the elderly people, identifying significant activities and places from GPS traces, parsing algorithms, Bayesian networks inference procedures and goal recognition for dialogue systems among others)
● Multiagent goal and plan recognition
● Domain design for goal and plan recognition
● Recognition of goals and plans of humans vs. virtual agents.
● Goal acquisition/elicitation for goal/plan recognition
● Activity recognition in isolation or integrated with Goal and Plan Recognition
Keywords: Goal Recognition, Plan Recognition, Multiagent Goal, Goal Acquisition, Automated Planning, Machine Learning
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
