The apparent action causation: Using a magician forcing technique to investigate our illusory sense of agency over the outcome of our choices

We often fall victim of an illusory sense of control and agency over our thoughts and actions. Magicians are masters at exploiting these illusions and forcing techniques provide a powerful way to study apparent action causation – the illusion that our action caused the outcome we get. In this paper, we used the Criss-Cross force to study whether people can tell the difference between an action which had an impact on the outcome they get and one which has no impact. In the Criss-Cross force, participants are asked to cut to a card, and whilst they are genuinely free to cut the cards at any position, the cut does not impact the card they are given (i.e. they always get the top card). We investigate the psychological processes that underpin the success of this force. Experiment 1 (N=60) showed that participants cannot tell the difference between a forced and a controlled outcome. Experiment 2 (N=90) showed that contrary to common magicians’ knowledge, misdirection does not play a role in the success of the force. Finally, experiment 3 (N=60) suggests that rather than misdirection, an attribute substitution error explains why people fail to understand that their action do not have an impact on the outcome they get. Debriefings also shows the importance of participants’ expectations in the perception of the trick, as well as the role of prediction of the outcome in participants’ sense of agency over the events

Tool support for reasoning in display calculi

We present a tool for reasoning in and about propositional sequent calculi. One aim is to support reasoning in calculi that contain a hundred rules or more, so that even relatively small pen and paper derivations become tedious and error prone. As an example, we implement the display calculus D.EAK of dynamic epistemic logic. Second, we provide embeddings of the calculus in the theorem prover Isabelle for formalising proofs about D.EAK. As a case study we show that the solution of the muddy children puzzle is derivable for any number of muddy children. Third, there is a set of meta-tools, that allows us to adapt the tool for a wide variety of user defined calculi

Cut-elimination for the mu-calculus with one variable

We establish syntactic cut-elimination for the one-variable fragment of the modal mu-calculus. Our method is based on a recent cut-elimination technique by Mints that makes use of Buchholz' Omega-rule.Comment: In Proceedings FICS 2012, arXiv:1202.317

Explicit Evidence Systems with Common Knowledge

Justification logics are epistemic logics that explicitly include justifications for the agents' knowledge. We develop a multi-agent justification logic with evidence terms for individual agents as well as for common knowledge. We define a Kripke-style semantics that is similar to Fitting's semantics for the Logic of Proofs LP. We show the soundness, completeness, and finite model property of our multi-agent justification logic with respect to this Kripke-style semantics. We demonstrate that our logic is a conservative extension of Yavorskaya's minimal bimodal explicit evidence logic, which is a two-agent version of LP. We discuss the relationship of our logic to the multi-agent modal logic S4 with common knowledge. Finally, we give a brief analysis of the coordinated attack problem in the newly developed language of our logic

Properties of ABA+ for Non-Monotonic Reasoning

We investigate properties of ABA+, a formalism that extends the well studied structured argumentation formalism Assumption-Based Argumentation (ABA) with a preference handling mechanism. In particular, we establish desirable properties that ABA+ semantics exhibit. These pave way to the satisfaction by ABA+ of some (arguably) desirable principles of preference handling in argumentation and nonmonotonic reasoning, as well as non-monotonic inference properties of ABA+ under various semantics.Comment: This is a revised version of the paper presented at the worksho

Food Safety Risk in an Indoor Microgreen Cultivation System

Microgreens are immature sprouts of edible plants, sharing some similarities with sprouted seeds and petite leafy greens. Since they are most often grown in containers in buildings or greenhouses, they present a new area for food safety research at the intersection of the built environment and produce farming. Contamination by human pathogens has been extensively studied in other types of produce typically eaten raw, including sprouted seeds, which have been implicated in numerous outbreaks of salmonellosis over the last several decades. There is a paucity of knowledge about the microgreen sector of the fresh-cut industry; thus, it was determined that a survey of operational details, microgreen varieties grown, and food safety practices would be needed to determine research directions. Following a nationwide survey of US-based microgreen farmers, two laboratory experiments were conducted using the most common production system type and microgreen varieties. Soil-free growing media (SFGM) was inoculated with Listeria monocytogenes FSL R2-574 and Salmonella enterica Javiana in a plant-free bench scale experiment as well as during cultivation of sunflower microgreens in a fully indoor, artificially lit, stacked track system similar to that of the microgreen farmers surveyed. It was found that the type of SFGM influenced survival of these two pathogens, which are commonly associated with sprouted seed outbreaks as well as several recent microgreen product recalls. Furthermore, it was found that survival of these pathogens was enhanced in the presence of the microgreen root environment. These results are important for informing system design decisions by microgreen farmers

Learning the Semantics of Manipulation Action

In this paper we present a formal computational framework for modeling manipulation actions. The introduced formalism leads to semantics of manipulation action and has applications to both observing and understanding human manipulation actions as well as executing them with a robotic mechanism (e.g. a humanoid robot). It is based on a Combinatory Categorial Grammar. The goal of the introduced framework is to: (1) represent manipulation actions with both syntax and semantic parts, where the semantic part employs $\lambda$-calculus; (2) enable a probabilistic semantic parsing schema to learn the $\lambda$-calculus representation of manipulation action from an annotated action corpus of videos; (3) use (1) and (2) to develop a system that visually observes manipulation actions and understands their meaning while it can reason beyond observations using propositional logic and axiom schemata. The experiments conducted on a public available large manipulation action dataset validate the theoretical framework and our implementation