Prof. Dechter's research is focused on automated reasoning in Artificial Intelligence, particularly in the areas of search, constraint-based reasoning and reasoning under uncertainty.

Her ongoing focus is on constraint processing, which emerges as a unifying theme that cuts across many traditional areas in Artificial Intelligence. A variety of techniques have been developed for processing different kinds of constraint expressions, and are being applied to diverse tasks such as vision, design, diagnosis, truth maintenance, scheduling, spatio-temporal reasoning, logic programming, and user interface. Many of these methods were incorporated into constraint programming languages which enhance practical applications substantially.

Since most reasoning tasks are computationally intractable, the primary aim of Prof. Dechter's research is to devise methods through the understanding and exploitation of tractable reasoning tasks. Her previous works on greedy problems, the mechanical generation of heuristics, the identification of tractable constraint models via topological decompositions, and the establishment of boundaries of local computations, have been driven by this principal concern. Dechter analyzes algorithms both analytically and empirically using real life applications such as scheduling, planning, and diagnosis.

Dr. Dechter's current focus is on extending the constraint model to new areas of reasoning, especially to reasoning under uncertainty, through an algorithmic framework called bucket elimination. This framework unifies dynamic programming for combinatorial optimization with algorithms for theorem proving, logic programs, temporal reasoning, probabilistic reasoning and planning under uncertainty. Within this framework she develops efficient (exact and approximate) reasoning algorithms guided by the domain's properties and applies those to areas such as medical diagnosis, probabilistic decoding.