Causal knowledge extraction through large-scale text mining
Oktie Hassanzadeh, Debarun Bhattacharjya, et al.
AAAI 2020
Recently there has been a lot of focus on human robot co-habitation issues that are often orthogonal to many aspects of human-robot teaming; e.g. on producing socially acceptable behaviors of robots and de-conflicting plans of robots and humans in shared environments. However, an interesting offshoot of these settings that has largely been overlooked is the problem of planning for serendipity - i.e. planning for stigmergic collaboration without explicit commitments on agents in co-habitation. In this paper we formalize this notion of planning for serendipity for the first time, and provide an Integer Programming based solution for this problem. Further, we illustrate the different modes of this planning technique on a typical Urban Search and Rescue scenario and show a real-life implementation of the ideas on the Nao Robot interacting with a human colleague.
Oktie Hassanzadeh, Debarun Bhattacharjya, et al.
AAAI 2020
Michele Berlingerio, Veli Bicer, et al.
ECML PKDD 2015
Neelamadhav Gantayat, Avirup Saha, et al.
CODS 2025
Pablo Fernandez Saura, Jayaram Kr Kallapalayam Radhakrishnan, et al.
SSE 2025