| 1. |
- Alomari, Muhannad, et al.
(författare)
-
Grounding of human environments and activities for autonomous robots
- 2017
-
Ingår i: Proceedings Of The Twenty-Sixth International Joint Conference On Artificial Intelligence. - California : International Joint Conferences on Artificial Intelligence. ; , s. 1395-1402
-
Konferensbidrag (refereegranskat)abstract
- With the recent proliferation of human-oriented robotic applications in domestic and industrial scenarios, it is vital for robots to continually learn about their environments and about the humans they share their environments with. In this paper, we present a novel, online, incremental framework for unsupervised symbol grounding in real-world, human environments for autonomous robots. We demonstrate the flexibility of the framework by learning about colours, people names, usable objects and simple human activities, integrating stateofthe-art object segmentation, pose estimation, activity analysis along with a number of sensory input encodings into a continual learning framework. Natural language is grounded to the learned concepts, enabling the robot to communicate in a human-understandable way. We show, using a challenging real-world dataset of human activities as perceived by a mobile robot, that our framework is able to extract useful concepts, ground natural language descriptions to them, and, as a proof-ofconcept, generate simple sentences from templates to describe people and the activities they are engaged in.
|
|
| 2. |
- Dubba, Krishna Sandeep Reddy, et al.
(författare)
-
Interleaved Inductive-Abductive Reasoning for Learning Complex Event Models
- 2011
-
Ingår i: Inductive Logic Programming. - Berlin, Heidelberg : Springer. - 9783642319501 - 9783642319518 ; , s. 113-129
-
Konferensbidrag (refereegranskat)abstract
- We propose an interleaved inductive-abductive model for reasoning about complex spatio-temporal narratives. Typed Inductive Logic Programming (Typed-ILP) is used as a basis for learning the domain theory by generalising from observation data, whereas abductive reasoning is used for noisy data correction by scenario and narrative completion thereby improving the inductive learning to get semantically meaningful event models. We apply the model to an airport domain consisting of video data for 15 turn-arounds from six cameras simultaneously monitoring logistical processes concerned with aircraft arrival, docking, departure etc and a verbs data set with 20 verbs enacted out in around 2500 vignettes. Our evaluation and demonstration focusses on the synergy afforded by the inductive-abductive cycle, whereas our proposed model provides a blue-print for interfacing common-sense reasoning about space, events and dynamic spatio-temporal phenomena with quantitative techniques in activity recognition.
|
|
