Placing post-graffiti: the journey of the Peckham Rock

This article is about the intersections between contemporary forms of urban inscription, art and the city, as they come to be configured through an emergent `post-graffiti' aesthetic practice. Exemplary of this movement is the self-proclaimed `art terrorist', Banksy, who has earned a reputation recently for his audacious interventions into some of the most significant art institutions in the western world, as well as for his politically charged stencil and sculptural work in the everyday spaces of the city. Focusing on the artist's Peckham Rock, a fragment of concrete that he surreptitiously stuck to the walls of the British Museum in May 2005, this article uses the methodological device of `the journey' in an attempt to place the connections and disconnections between a series of elite and institutional spaces, social relations and mediascapes through which `the rock' passes as its `life' as an artwork unfolds. Existing research, including that by geographers, has examined graffiti in terms of urban identity politics, territoriality and transgression. While such work has generated important insights into the nature of particular kinds of urbanism, it is often limited to a focus on graffiti `writing', a subcultural model of urban inscription originating in New York and Philadelphia in the late 1960s. In contrast, this article explores a more recent style of inscribing the city, as set out in a series of art publications and conferences, and unpacks what such a model might indicate regarding contemporary urban processes and experiences

An Answer Set Programming-based Implementation of Epistemic Probabilistic Event Calculus

We describe a general procedure for translating Epistemic Probabilistic Event Calculus (EPEC) action language domains into Answer Set Programs (ASP), and show how the Python-driven features of the ASP solver Clingo can be used to provide efficient computation in this probabilistic setting. EPEC supports probabilistic, epistemic reasoning in domains containing narratives that include both an agent’s own action executions and environmentally triggered events. Some of the agent’s actions may be belief-conditioned, and some may be imperfect sensing actions that alter the strengths of previously held beliefs. We show that our ASP implementation can be used to provide query answers that fully correspond to EPEC’s own declarative, Bayesian-inspired semantics

Dreamlands: stories of enchantment and excess in a search for lost sensations

This paper reflects on the search for a lost, obscure piece of experimental architecture that appeared on the west coast of Scotland in the late 1960s. Encouraged by cultural geography’s efforts to recuperate storytelling as a valid mode of inquiry and to adopt a more enchanted, affirmative disposition to our endeavors, we develop a geographical story intended to draw out how enchanted experiences gained through curiosity and an openness to contingencies can serve as a vital force for sustaining geographical ways of being, doing and knowing with the world. This account focuses on our encounters with various research sites that we identify as ‘dreamlands’ to express the idiosyncratic, unregulated, unexpected sensations of wonder and delight that such places evoked, the excessive materialities they revealed and the imaginative processes they elicited. We argue that such dreamlands are not as superfluous as might be assumed by their uncanny absence from the polished end-products of scholarship, and instead, allude to the latent forces of enchantment to which geographers might become better attuned when conducting and crafting their research

An answer set programming-based implementation of epistemic probabilistic event calculus

We describe a general procedure for translating Epistemic Probabilistic Event Calculus (EPEC) action language domains into Answer Set Programs (ASP), and show how the Python-driven features of the ASP solver Clingo can be used to provide efficient computation in this probabilistic setting. EPEC supports probabilistic, epistemic reasoning in domains containing narratives that include both an agent's own action executions and environmentally triggered events. Some of the agent's actions may be belief-conditioned, and some may be imperfect sensing actions that alter the strengths of previously held beliefs. We show that our ASP implementation can be used to provide query answers that fully correspond to EPEC's own declarative, Bayesian-inspired semantics

A Graphical Formalism for Commonsense Reasoning with Recipes

Whilst cooking is a very important human activity, there has been little consideration given to how we can formalize recipes for use in a reasoning framework. We address this need by proposing a graphical formalization that captures the comestibles (ingredients, intermediate food items, and final products), and the actions on comestibles in the form of a labelled bipartite graph. We then propose formal definitions for comparing recipes, for composing recipes from subrecipes, and for deconstructing recipes into subrecipes. We also introduce and compare two formal definitions for substitution into recipes which are required when there are missing ingredients, or some actions are not possible, or because there is a need to change the final product somehow

PizzaCommonSense: Learning to Model Commonsense Reasoning about Intermediate Steps in Cooking Recipes

Decoding the core of procedural texts, exemplified by cooking recipes, is crucial for intelligent reasoning and instruction automation. Procedural texts can be comprehensively defined as a sequential chain of steps to accomplish a task employing resources. From a cooking perspective, these instructions can be interpreted as a series of modifications to a food preparation, which initially comprises a set of ingredients. These changes involve transformations of comestible resources. For a model to effectively reason about cooking recipes, it must accurately discern and understand the inputs and outputs of intermediate steps within the recipe. Aiming to address this, we present a new corpus of cooking recipes enriched with descriptions of intermediate steps of the recipes that explicate the input and output for each step. We discuss the data collection process, investigate and provide baseline models based on T5 and GPT-3.5. This work presents a challenging task and insight into commonsense reasoning and procedural text generation.Comment: The data is available at: https://github.com/adiallo07/PizzaCommonsens