A formal theory of plan recognition
A formal theory of plan recognition
Modeling the user's plans and goals
Computational Linguistics - Special issue on user modeling
A plan-based intelligent assistant that supports the software development
SDE 3 Proceedings of the third ACM SIGSOFT/SIGPLAN software engineering symposium on Practical software development environments
The berkeley UNIX consultant project
Computational Linguistics
ADL: exploring the middle ground between STRIPS and the situation calculus
Proceedings of the first international conference on Principles of knowledge representation and reasoning
A Bayesian model of plan recognition
Artificial Intelligence
Fast planning through planning graph analysis
Artificial Intelligence
Acquisition of abstract plan descriptions for plan recognition
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Bayesian Models for Keyhole Plan Recognition in an Adventure Game
User Modeling and User-Adapted Interaction
Graph Construction and Analysis as a Paradigm for Plan Recognition
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Extending Planning Graphs to an ADL Subset
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Combining the Expressivity of UCPOP with the Efficiency of Graphplan
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
User models: the problem of disparity
COLING '86 Proceedings of the 11th coference on Computational linguistics
A sound and fast goal recognizer
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
The BATmobile: towards a Bayesian automated taxi
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Accounting for context in plan recognition, with application to traffic monitoring
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Graphical user interface of an interactive system for schemes design, used in distance learning
CompSysTech '04 Proceedings of the 5th international conference on Computer systems and technologies
A KEYHOLE PLAN RECOGNITION MODEL FOR ALZHEIMER'S PATIENTS: FIRST RESULTS
Applied Artificial Intelligence
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Corpus-based, statistical goal recognition
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Domain Independent Goal Recognition
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
A plan classifier based on Chi-square distribution tests
Intelligent Data Analysis
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
Mining service integration opportunities towards joined-up government
Proceedings of the 5th International Conference on Theory and Practice of Electronic Governance
Flexible goal recognition via graph construction and analysis
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Modeling sequences of user actions for statistical goal recognition
User Modeling and User-Adapted Interaction
Location-based reasoning about complex multi-agent behavior
Journal of Artificial Intelligence Research
State-of-the-art of intention recognition and its use in decision making
AI Communications
Intelligent Decision Technologies
Hi-index | 0.00 |
We present a novel approach to goal recognition based on a two-stage paradigm of graph construction and analysis. First, a graph structure called a Goal Graph is constructed to represent the observed actions, the state of the world, and the achieved goals as well as various connections between these nodes at consecutive time steps. Then, the Goal Graph is analysed at each time step to recognise those partially or fully achieved goals that are consistent with the actions observed so far. The Goal Graph analysis also reveals valid plans for the recognised goals or part of these goals. Our approach to goal recognition does not need a plan library. It does not suffer from the problems in the acquisition and hand-coding of large plan libraries, neither does it have the problems in searching the plan space of exponential size. We describe two algorithms for Goal Graph construction and analysis in this paradigm. These algorithms are both provably sound, polynomial-time, and polynomial-space. The number of goals recognised by our algorithms is usually very small after a sequence of observed actions has been processed. Thus the sequence of observed actions is well explained by the recognised goals with little ambiguity. We have evaluated these algorithms in the UNIX domain, in which excellent performance has been achieved in terms of accuracy, efficiency, and scalability.