Ashiqur R. KhudaBukhsh

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Designing high-performance algorithms for computation-ally hard problems is a difficult and often time-consuming task. In this work, we demonstrate that this task can be automated in the context of stochastic local search (SLS) solvers for the propositional satisfiability problem (SAT). We first introduce a generalised, highly param-eterised solver(More)
A group of agents are said to collude if they share information or make joint decisions in a manner contrary to explicit or implicit social rules that results in an unfair advantage over non-colluding agents or other interested parties. For instance, collusion manifests as sharing answers in exams, as colluding bidders in auctions, or as colluding(More)
Query-based triggers play a crucial role in modern search systems, e.g., in deciding when to display direct answers on result pages. We address a common scenario in designing such triggers for real-world settings where positives are rare and search providers possess only a small seed set of positive examples to learn query classification models. We choose(More)
Human experts as autonomous agents in a referral network must decide whether to accept a task or refer to a more appropriate expert, and if so to whom. In order for the referral network to improve over time, the experts must learn to estimate the topical expertise of other experts. This thesis extends concepts from Multi-agent Reinforcement Learning and(More)
Supermodular games are an interesting class of games that exhibits strategic complementarity. There are several compelling reasons like existence of pure strategy nash equilibrium, dominance solvability, identical bounds on joint strategy space etc. that make them a strong candidate for game theoretic modeling of economics. Supermodular games give a sound(More)
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