Multiagent systems offer a design paradigm used to conceptualise and implement systems composed of autonomous agents. Autonomy facilitates proactive independent behaviour yet in practice agents are constrained in order to ensure the system satisfies a desired social objective. Explicit constraints on agent behaviour, in the form of social norms, encourage this desirable system behaviour, yet research has largely focused on norm representation languages and protocols for norm proposal and adoption. The fundamental problem of how to automate the process of norm synthesis has largely been overlooked with norms assumed provided by the designer. Previous work has shown that automating the design of social norms is intractable in the worst case. Existing approaches, relying on state space enumerations, are effective for small systems but impractical for larger ones. Furthermore, they do not produce a set of succinct, general norms but rather a large number of state-specific restrictions. This work presents conflict-rooted synthesis, an automated norm synthesis approach that utilises a planning-based action schemata to overcome these limitations. These action schemata facilitate localised searches around specifications of undesirable states, using representations of sets of system states to avoid a full state enumeration. The proposed technique produces concise, generalised social norms that are applicable in multiple system states while also providing guarantees that agents are still able to achieve their original goals in the constrained system. To improve efficiency a set of theoretically sound, domain-independent optimisations are presented that reduce the state space searched without compromising the quality of the norms synthesised. A comparison with an alternative model checking based technique illustrates the advantages and disadvantages of our approach, while an empirical evaluation highlights the improved efficiency and quality of norms it produces at the cost of a less expressive specification of undesirable states. We empirically investigate the effectiveness of each of the proposed optimisations using a set of benchmark domains, quantifying how successful each of them is at reducing search complexity in practice. The results show that, with all optimisations enabled, conflict-rooted synthesis produces more generally applicable and succinct norms and consumes fewer system resources. Additionally, we show that this approach synthesises norms in systems where the competing approach is intractable. We provide a discussion of our approach, highlighting the impact our abstract search approach has on the fields of multiagent systems and automated planning, and discuss the limitations and assumptions we have made. We conclude with a presentation of future work.