Reasoning on Conflicting situations for Ambient Assisted Living
The execution of temporal projection tasks represents a popular formal technique in the field of cognitive robotics, while its practical significance is also evidenced in recent implementations of autonomous systems. Its use in AmI domains can provide an extra leverage in achieving proactive behavior. The following example demonstrates a case that has been modeled in Event calculus and implemented and experimented in the living lab. Imagine that the inhabitant, while being at the kitchen, turns on the Kettle, Oven or hot plate and places a pot containing milk. The content of the pot will begin to heat up and eventually start to boil. There is no dedicated sensor measuring the temperature of the milk; we rely on commonsense derivations in order to model this behavior. As such, the system can also expect that the boiling point will be reached after a while, and therefore sets a timer to become aware of this incident. The objective of the monitoring system is to identify potentially conflicting situations both at the present time and in the future, in order to trigger different types of alerts and recommendations in an as less intrusive manner as possible. In this case, by performing temporal projection with a time window of more than 3 minutes the system will predict that the milk will start to boil; yet, since the user can reasonably be assumed to be in the kitchen, no preventive action needs to be taken and any alert can be postponed until a later point.
Returning in real-time mode, imagine that the inhabitant then leaves the kitchen, enters the bathroom and turns on the bathtub faucet causing water to start filling the bathtub. A progression of the world state now will allow the system to identify that the two parallel activities of milk heating up and water filling the bathtub will demand the user’s attention at approximately the same time at two different locations: he should stop the water from reaching the rim of the bathtub while also turn off the hot plate in the kitchen. Although the critical situation refers to a future point in time and it is not certain that it will actually occur, a warning message is more appropriate to be placed in the present state. The reasoning system will infer on the following: move the robot to the inhabitant current location and initiate an audio message to warn him about the situation and suggest him to turn off the kettle, the oven or the hot plate. In different cases, the reasoning system may instead decide to take initiative, such as to turn off the appliance on the inhabitant's behalf.
References:
T. Patkos, D. Plexousakis, A. Chibani, and Y. Amirat, "An Event Calculus production rule system in dynamic and uncertain domains," Journal of Theory and Practice of Logic Programming, Cambridge University Press, vol. 16, no. 3, pp. 325-352, 2016. .
A. Chibani, A. Bikakis, T. Patkos, Y. Amirat, S. Bouznad, N. Ayari, and L. Sabri, "Using Cognitive Ubiquitous Robots for Assisting Dependent People in Smart Spaces," in Intelligent Assistive Robots- Recent advances in assistive robotics for everyday activities, S. Mohammed and J. C. Moreno and K. Kong and Y. Amirat Eds, Springer Tracts on Advanced Robotics (STAR) series, 2015, pp. 297-316. .
T. Patkos, A. Chibani, D. Plexousakis, and Y. Amirat, "A Production Rule-based Framework for Causal and Epistemic Reasoning," in Proc. Of the RuleML Symposium held in conjunction with ECAI 2012, the 20th biennial European Conference on Artificial Intelligence, Montpellier, France, 2012, pp. 120-135. .
B. Hu, T. Patkos, A. Chibani, and Y. Amirat, "Rule-Based Context Assessment in Smart Cities," in Proc. Of the 6th International Conference on Web Reasoning and Rule Systems, RR 2012, Vienna, Austria, 2012, pp. 221-224. .
B. Hu, A. Chibani, and Y. Amirat, "Semantic context relevance assessment in urban ubiquitous environments," in Proc. Of the 14th International conference on Ubiquitous Computing UbiComp'12, Pittsburgh, United States, 2012, pp. 639-640. .