Learning Hierarchical Task Networks Using Semantic Word Embeddings

This thesis describes WORD2HTN, which is a novel and semantic approach for learning hierarchical task networks (HTN) and semantic division of goals from input plan traces. The semantic relationships are learned using machine learning to get the vector representations of the components of the plan trace. The semantic relationships are used to learn hierarchical landmarks, which in turn are used to make semantically divided HTNs. These learned HTNs can then be used for subsequent new problems in the domain that have a similar structure with the problems in the input plan traces. This work also improves the learning algorithm to include arithmetic conditions and effects. WORD2HTN was tested on 3 deterministic domains. These are Logistics or Transportation domain, Abstract Graph domain, and the Malmo interface for the Minecraft game. We show that WORD2HTN learns semantically divided HTNs. We also experimentally demonstrate that HTN planners using this have an exponential speedup in information-dense domains over the state of the art classical planner. Finally, we show that the HTNs learned in Minecraft can be used to achieve tasks faster with a cooperative agent controlled by the HTN planner’s output

In vitro comparism of the extracellular secretion of inulosucrase enzyme in potential probiotic Escherichia coli 16 and BL-21

Escherichia coli 16 has potential probiotic properties including antimicrobial activity due to extracellular secretion of colicins E1/1a1b. Inulosucrase (InuJ) enzyme catalyses the polymerization of a fructose moiety of sucrose leading to the formation of fructooligosaccharides. The present investigation compared the activity of InuJ enzymes cloned into pMAL-p2ΔlacIQ a deletion vector and transformed into E. coli 16 and standard strain that is, E. coli BL21. Specific activities of InuJ enzyme were estimated in supernatant, periplasm and lysate. Specific activities of InuJ activity in cell lysate were similar in E. coli 16 and E. coli BL21 without induction of tac promoter with isopropyl thio-β-Dgalactoside (IPTG). InuJ activity is mainly present in the periplasm of E. coli BL21 whereas in E. coli 16, most of the activity is in the supernatant. Superantant of E. coli 16 strain also showed good antibacterial activity due to colicin E1/Ia1b. Colicin E1/1a1b transport system could allow extracellular secretion of InuJ proteins in probiotic E. coli 16.Key words: Colicin, extracellular, E. coli, fructooligosaccharide, inulosucrase, prebiotic, probiotic

SafeAR: Towards Safer Algorithmic Recourse by Risk-Aware Policies

With the growing use of machine learning (ML) models in critical domains such as finance and healthcare, the need to offer recourse for those adversely affected by the decisions of ML models has become more important; individuals ought to be provided with recommendations on actions to take for improving their situation and thus receive a favorable decision. Prior work on sequential algorithmic recourse -- which recommends a series of changes -- focuses on action feasibility and uses the proximity of feature changes to determine action costs. However, the uncertainties of feature changes and the risk of higher than average costs in recourse have not been considered. It is undesirable if a recourse could (with some probability) result in a worse situation from which recovery requires an extremely high cost. It is essential to incorporate risks when computing and evaluating recourse. We call the recourse computed with such risk considerations as Safer Algorithmic Recourse (SafeAR). The objective is to empower people to choose a recourse based on their risk tolerance. In this work, we discuss and show how existing recourse desiderata can fail to capture the risk of higher costs. We present a method to compute recourse policies that consider variability in cost and connect algorithmic recourse literature with risk-sensitive reinforcement learning. We also adopt measures ``Value at Risk'' and ``Conditional Value at Risk'' from the financial literature to summarize risk concisely. We apply our method to two real-world datasets and compare policies with different levels of risk-aversion using risk measures and recourse desiderata (sparsity and proximity).Comment: Supplemental material appended to main pape