Thomas Cole and the Landscape of Evangelical Print

This chapter defines the central concept of evangelical space. It shows how the evangelical print that flooded the nation in the 1820s and 1830s combined with an emerging landscape art culture that produced spectacular visualizations of the evangelical spatial imagination. It also mentions illustrated religious tracts, almanacs, and Bibles alongside one of the most iconic landscape paintings of the period, Thomas Cole's View from Mount Holyoke, Northampton, Massachusetts, after a Thunderstorm. The chapter reveals that landscape art enacted a symbolic synthesis of two competing impulses in northern evangelical culture: the individual believer's inward pilgrimage toward God and the collective work of global missionary activism. It reconstructs an immersive form of looking closely that is tied to evangelical reading practices and shows how devout viewers used the landscape to orient themselves in sacred history.

Symbolic Synthesis of Fault-Tolerance Ratios in Parameterised Multi-Agent Systems

We study the problem of determining the robustness of a multi-agent system of unbounded size against specifications expressed in a temporal-epistemic logic. We introduce a procedure to synthesise automatically the maximal ratio of faulty agents that may be present at runtime for a specification to be satisfied in a multi-agent system. We show the procedure to be sound and amenable to symbolic implementation. We present an implementation and report the experimental results obtained by running this on a number of protocols from swarm robotics.

Symbolic LTLf Synthesis

LTLf synthesis is the process of finding a strategy that satisfies a linear temporal specification over finite traces. An existing solution to this problem relies on a reduction to a DFA game. In this paper, we propose a symbolic framework for LTLf synthesis based on this technique, by performing the computation over a representation of the DFA as a boolean formula rather than as an explicit graph. This approach enables strategy generation by utilizing the mechanism of boolean synthesis. We implement this symbolic synthesis method in a tool called Syft, and demonstrate by experiments on scalable benchmarks that the symbolic approach scales better than the explicit one.