An LP-Based Approach for Goal Recognition as Planning

Goal recognition aims to recognize the set of candidate goals that are compatible with the observed behavior of an agent. In this paper, we develop a method based on the operator-counting framework that efficiently computes solutions that satisfy the observations and uses the information generated to solve goal recognition tasks. Our method reasons explicitly about both partial and noisy observations: estimating uncertainty for the former, and satisfying observations given the unreliability of the sensor for the latter. We evaluate our approach empirically over a large data set, analyzing its components on how each can impact the quality of the solutions. In general, our approach is superior to previous methods in terms of agreement ratio, accuracy, and spread. Finally, our approach paves the way for new research on combinatorial optimization to solve goal recognition tasks.Comment: 8 pages, 4 tables, 3 figures. Published in AAAI 2021. Updated final authorship and tex

Relational Reasoning Network (RRN) for Anatomical Landmarking

Accurately identifying anatomical landmarks is a crucial step in deformation analysis and surgical planning for craniomaxillofacial (CMF) bones. Available methods require segmentation of the object of interest for precise landmarking. Unlike those, our purpose in this study is to perform anatomical landmarking using the inherent relation of CMF bones without explicitly segmenting them. We propose a new deep network architecture, called relational reasoning network (RRN), to accurately learn the local and the global relations of the landmarks. Specifically, we are interested in learning landmarks in CMF region: mandible, maxilla, and nasal bones. The proposed RRN works in an end-to-end manner, utilizing learned relations of the landmarks based on dense-block units and without the need for segmentation. For a given a few landmarks as input, the proposed system accurately and efficiently localizes the remaining landmarks on the aforementioned bones. For a comprehensive evaluation of RRN, we used cone-beam computed tomography (CBCT) scans of 250 patients. The proposed system identifies the landmark locations very accurately even when there are severe pathologies or deformations in the bones. The proposed RRN has also revealed unique relationships among the landmarks that help us infer several reasoning about informativeness of the landmark points. RRN is invariant to order of landmarks and it allowed us to discover the optimal configurations (number and location) for landmarks to be localized within the object of interest (mandible) or nearby objects (maxilla and nasal). To the best of our knowledge, this is the first of its kind algorithm finding anatomical relations of the objects using deep learning.Comment: 10 pages, 6 Figures, 3 Table

Rational coordination of crowdsourced resources for geo-temporal request satisfaction

Existing mobile devices roaming around the mobility field should be considered as useful resources in geo-temporal request satisfaction. We refer to the capability of an application to access a physical device at particular geographical locations and times as GeoPresence, and we pre- sume that mobile agents participating in GeoPresence-capable applica- tions should be rational, competitive, and willing to deviate from their routes if given the right incentive. In this paper, we define the Hitch- hiking problem, which is that of finding the optimal assignment of re- quests with specific spatio-temporal characteristics to competitive mobile agents subject to spatio-temporal constraints. We design a mechanism that takes into consideration the rationality of the agents for request sat- isfaction, with an objective to maximize the total profit of the system. We analytically prove the mechanism to be convergent with a profit com- parable to that of a 1/2-approximation greedy algorithm, and evaluate its consideration of rationality experimentally.Supported in part by NSF Grants; #1430145, #1414119, #1347522, #1239021, and #1012798