Developers of text generation models rely on automated evaluation metrics as a stand-in for slow and expensive manual evaluations. However, image captioning metrics have struggled to give accurate learned estimates of the semantic and pragmatic success of output text. We address this weakness by introducing the first discourse-aware learned generation metric for evaluating image descriptions. Our approach is inspired by computational theories of discourse for capturing information goals using coherence. We present a dataset of image$\unicode{x2013}$description pairs annotated with coherence relations. We then train a coherence-aware metric on a subset of the Conceptual Captions dataset and measure its effectiveness$\unicode{x2014}$its ability to predict human ratings of output captions$\unicode{x2014}$on a test set composed of out-of-domain images. We demonstrate a higher Kendall Correlation Coefficient for our proposed metric with the human judgments for the results of a number of state-of-the-art coherence-aware caption generation models when compared to several other metrics including recently proposed learned metrics such as BLEURT and BERTScore.

Configuration planning is a form of task planning that takes into consideration both causal and information dependencies in goal achievement. This type of planning is interesting, for instance, in smart home environments which contain various sensors and robots to provide services to the inhabitants. Requests for information, for instance from an activity recognition system, should cause the smart home to configure itself in such a way that all requested information will be provided when it is needed. This paper addresses temporal configuration planning in which information availability and goals are linked to temporal intervals which are subject to constrains. Our solutions are based on constraint-based planning which uses different types of constraints to model different types of knowledge. We propose and compare two approaches to configuration planning. The first one models information via conditions and effects of planning operators and essentially reduces configuration planning to constraint-based temporal planning. The second approach solves information dependencies separately from task planning and optimizes the cost of reaching individual information goals. We compare these approaches in terms of the time it takes to solve problems and the quality of the solutions they provide.