Goal Recognition Techniques in Artificial Intelligence Systems
Summary
Goal recognition is the process by which an artificial system infers the intended objective of an agent from partial observations of its actions and the state of the environment. Techniques span plan-library matching, in which observed behaviours are compared against a predefined set of possible plans, to planning-based inference that dynamically generates and evaluates candidate plans according to a rationality principle. Probabilistic frameworks, often based on Bayesian networks or hidden Markov models, explicitly account for sensor noise, suboptimal actions and missing data, while inverse planning and inverse reinforcement learning derive reward or utility functions that best explain the observed behaviour. Recent trends include the integration of deep-learning for high-dimensional sensory input, the treatment of temporally extended goals specified in temporal logics, and the consideration of multi-agent interactions and adversarial settings. Key challenges remain in managing non-deterministic dynamics, partial observability and scalability. Emerging approaches seek to combine symbolic and data-driven methods, yielding more robust, interpretable and efficient goal recognisers applicable to domains such as human–robot collaboration, security monitoring and adaptive assistance systems.
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Goal Recognition Techniques in Artificial Intelligence Systems publication trend
The graph below shows the total number of articles in goal recognition techniques in artificial intelligence systems across all publications each year (not limited to Nature Index journals).
Technical terms
Landmark: A propositional fact or action that must occur in any valid plan achieving a given goal, used to enhance discriminative features in recognition models.
Behavioural model: A representation of an agent’s plan execution, capturing sequences of actions and hidden intentions for inference.
Action Graph: A directed graph encoding order constraints among all possible actions, with nodes labelled by distance metrics to candidate goals for probabilistic inference.
Fluent: A state variable whose truth value can change over time, representing dynamic aspects of the environment in planning models.
Linear Temporal Logic (LTL): A formal language used to specify temporally extended objectives over sequences of states or actions in dynamic systems.
References
- Intention recognition for multiple agents. Information Sciences (2023).
- Cyclic Action Graphs for goal recognition problems with inaccurately initialised fluents. Knowledge and Information Systems (2023).
- Temporally extended goal recognition in fully observable non-deterministic domain models. Applied Intelligence (2023).
- Activity, Plan, and Goal Recognition: A Review. Frontiers in Robotics and AI (2021).
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