From image sequences towards conceptual descriptions
Image and Vision Computing
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Association of motion verbs with vehicle movements extracted from dense optical flow fields
ECCV '94 Proceedings of the third European conference on Computer Vision (Vol. II)
Fundamentals of speech synthesis and speech recognition: basic concepts, state-of-the-art and future challenges
Survey of the state of the art in human language technology
Survey of the state of the art in human language technology
Fundamenta Informaticae
Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maintaining knowledge about temporal intervals
Communications of the ACM
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Relational rule induction with CPROGO14.4: a tutorial introductuon
Relational Data Mining
Learning a Sparse Representation for Object Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Recognizing multitasked activities from video using stochastic context-free grammar
Eighteenth national conference on Artificial intelligence
Interval scripts: a programming paradigm for interactive environments and agents
Personal and Ubiquitous Computing
SIGGRAPH '78 Proceedings of the 5th annual conference on Computer graphics and interactive techniques
Temporal scene analysis: conceptual descriptions of object movements.
Temporal scene analysis: conceptual descriptions of object movements.
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Journal of Artificial Intelligence Research
Grounding the lexical semantics of verbs in visual perception using force dynamics and event logic
Journal of Artificial Intelligence Research
Navigating through logic-based scene models for high-level scene interpretations
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Hidden state and reinforcement learning with instance-based stateidentification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A simple yet effective technique for partitioning
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Not far away from home: a relational distance-based approach to understanding images of houses
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Predictive and descriptive approaches to learning game rules from vision data
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
Learning rules of a card game from video
Artificial Intelligence Review
Interleaved inductive-abductive reasoning for learning complex event models
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
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This paper presents a cognitive vision system capable of autonomously learning protocols from perceptual observations of dynamic scenes. The work is motivated by the aim of creating a synthetic agent that can observe a scene containing interactions between unknown objects and agents, and learn models of these sufficient to act in accordance with the implicit protocols present in the scene. Discrete concepts (utterances and object properties), and temporal protocols involving these concepts, are learned in an unsupervised manner from continuous sensor input alone. Crucial to this learning process are methods for spatio-temporal attention applied to the audio and visual sensor data. These identify subsets of the sensor data relating to discrete concepts. Clustering within continuous feature spaces is used to learn object property and utterance models from processed sensor data, forming a symbolic description. The progol Inductive Logic Programming system is subsequently used to learn symbolic models of the temporal protocols presented in the presence of noise and over-representation in the symbolic data input to it. The models learned are used to drive a synthetic agent that can interact with the world in a semi-natural way. The system has been evaluated in the domain of table-top game playing and has been shown to be successful at learning protocol behaviours in such real-world audio-visual environments.