Handling temporality in human activity reasoning

Human-aware Artificial Intelligent systems are goal directed autonomous systems that are capable of interacting, collaborating, and teaming with humans. Activity reasoning is a formal reasoning approach that aims to provide common sense reasoning capabilities to these interactive and intelligent systems. This reasoning can be done by considering evidences –which may be conflicting–related to activities a human performs. In this context, it is important to consider the temporality of such evidence in order to distinguish activities and to analyse the relations between activities. Our approach is based on formal argumentation reasoning, specifically, Timed Argumentation Frameworks (TAF), which is an appropriate technique for dealing with inconsistencies in knowledge bases. Our approach involves two steps: local selection and global selection. In the local selection, a model of the world and of the human’s mind is constructed in form of hypothetical fragments of activities (pieces of evidences) by considering a set of observations. These hypothetical fragments have two kinds of relations: a conflict relation and a temporal relation. Based on these relations, the argumentation attack notion is defined. We define two forms of attacks namely the strong and the weak attack. The former has the same characteristics of attacks in TAF whereas for the latter the TAF approach has to be extended. For determining consistent sets of hypothetical fragments, that are part of an activity or are part of a set of non-conflicting activities, extension-based argumentation semantics are applied. In the global selection, the degrees of fulfillment of activities is determined. We study some properties of our approach and apply it to a scenario where a human performs activities with different temporal relations.

COMPREHENSIVE ANALYSIS OF THE FORT INSTRUMENT: USING DISTRACTOR ANALYSIS TO EXPLORE STUDENTS’ SCIENTIFIC REASONING BASED ON ACADEMIC LEVEL AND GENDER DIFFERENCE

Scientific reasoning ability is essential to get developed in the current digital age, particularly in the process of judgement and decision-making in complex problems. Few studies have conducted an in-depth exploration of scientific reasoning ability, especially in relation to the confidence level and gender. The scientific reasoning ability of Indonesian upper-secondary school and university students were examined and compared with previous recorded data of US students. In this study, the data were collected from 372 university and 528 upper-secondary education students in Indonesia. Students’ scientific reasoning ability was measured using a scientific formal reasoning test (FORT). In addition, confidence level and metacognitive data was collected through self-reported measures. Two-way ANOVA was performed to compare mean differences between groups based on academic level and gender and to observe interaction between the variables. Students’ confidence level in selecting the correct answer and distractor answer was analyzed using an independent t-test. The results reveal that many Indonesian students selected specific distractors with relatively high confidence. Moreover, upper-secondary school students and female students selected more distractors than the groups’ counterparts. Finally, the factors related to Indonesian students’ responses to the scientific formal reasoning were discussed. Keywords: confidence level, distractor analysis, gender differences, scientific (formal) reasoning test, scientific reasoning ability, Indonesian student

Combining quantitative and qualitative reasoning in concurrent multi-player games

We propose a general framework for modelling and formal reasoning about multi-agent systems and, in particular, multi-stage games where both quantitative and qualitative objectives and constraints are involved. Our models enrich concurrent game models with payoffs and guards on actions associated with each state of the model and propose a quantitative extension of the logic $${\textsf {ATL}}^{*}$$ ATL ∗ that enables the combination of quantitative and qualitative reasoning. We illustrate the framework with some detailed examples. Finally, we consider the model-checking problems arising in our framework and establish some general undecidability and decidability results for them.

Modular specification and verification of closures in Rust

Closures are a language feature supported by many mainstream languages, combining the ability to package up references to code blocks with the possibility of capturing state from the environment of the closure's declaration. Closures are powerful, but complicate understanding and formal reasoning, especially when closure invocations may mutate objects reachable from the captured state or from closure arguments. This paper presents a novel technique for the modular specification and verification of closure-manipulating code in Rust. Our technique combines Rust's type system guarantees and novel specification features to enable formal verification of rich functional properties. It encodes higher-order concerns into a first-order logic, which enables automation via SMT solvers. Our technique is implemented as an extension of the deductive verifier Prusti, with which we have successfully verified many common idioms of closure usage.

Ontology Reasoning for Explanatory Feedback Generation to Teach How Algorithms Work

Developing algorithms using control structures and understanding their building blocks are essential skills in mastering programming. Ontologies and software reasoning is a promising method for developing intelligent tutoring systems in well-defined domains (like programming languages and algorithms); it can be used for many kinds of teaching tasks. In this work, we used a formal model consisting of production rules for Apache Jena reasoner as a basis for developing a constraint-based tutor for introductory programming domain. The tutor can determine fault reasons for any incorrect answer that a student can enter. The problem the student should solve is building an execution trace for the given algorithm. The problem is a closed-ended question that requires arranging given actions in the (unique) correct order; some actions can be used several times, while others can be omitted. Using formal reasoning to check domain constraints allowed us to provide explanatory feedback for all kinds of errors students can make.

Logic and Interpretation: Syllogistic Reconstructions in Simplicius’ Commentary on Aristotle’s Physics

In this article I explain three puzzling features of Simplicius’ use of syllogistic reconstructions in his commentary on Aristotle’s Physics: (1) Why does he reconstruct Aristotle’s non-argumentative remarks? (2) Why does he identify the syllogistic figure of an argument but does not explicitly present its reconstruction? (3) Why in certain lemmata does he present several reconstructions of the same argument? Addressing these questions, I argue that these puzzling features are an expression of Simplicius’ assumption that formal reasoning underlies Aristotle’s prose, hence they reflect his attempt to capture as faithfully as possible Aristotle’s actual mode of reasoning. I show further that, as a consequence of this seemingly descriptive use of syllogistic reconstructions, logic serves Simplicius not only as an expository and clarificatory tool of certain interpretations or philosophical views, but also motivates and shapes his exegetical stances and approach.

Formal verification of a concurrent bounded queue in a weak memory model

We use Cosmo, a modern concurrent separation logic, to formally specify and verify an implementation of a multiple-producer multiple-consumer concurrent queue in the setting of the Multicore OCaml weak memory model. We view this result as a demonstration and experimental verification of the manner in which Cosmo allows modular and formal reasoning about advanced concurrent data structures. In particular, we show how the joint use of logically atomic triples and of Cosmo's views makes it possible to describe precisely in the specification the interaction between the queue library and the weak memory model.

Argumentation schemes for clinical decision support

This paper demonstrates how argumentation schemes can be used in decision support systems that help clinicians in making treatment decisions. The work builds on the use of computational argumentation, a rigorous approach to reasoning with complex data that places strong emphasis on being able to justify and explain the decisions that are recommended. The main contribution of the paper is to present a novel set of specialised argumentation schemes that can be used in the context of a clinical decision support system to assist in reasoning about what treatments to offer. These schemes provide a mechanism for capturing clinical reasoning in such a way that it can be handled by the formal reasoning mechanisms of formal argumentation. The paper describes how the integration between argumentation schemes and formal argumentation may be carried out, sketches how this is achieved by an implementation that we have created and illustrates the overall process on a small set of case studies.

Modeling the C. elegans Germline Stem Cell Genetic Network using Automated Reasoning

Computational methods and tools are a powerful complementary approach to experimental work for studying regulatory interactions in living cells and systems. We demonstrate the use of formal reasoning methods as applied to the Caenorhabditis elegans germ line, which is an accessible model system for stem cell research. The dynamics of the underlying genetic networks and their potential regulatory interactions are key for understanding mechanisms that control cellular decision making between stem cells and differentiation.We model the 'stem cell fate' versus entry into the 'meiotic development' pathway decision circuit in the young adult germ line based on an extensive study of published experimental data and known/hypothesized genetic interactions. We apply a formal reasoning framework to derive predictive networks for control of differentiation. Using this approach we simultaneously specify many possible scenarios and experiments together with potential genetic interactions, and synthesize genetic networks consistent with all encoded experimental observations. In silico analysis of knock-down and overexpression experiments within our model recapitulate published phenotypes of mutant animals and can be applied to make predictions on cellular decision-making. This work lays a foundation for developing realistic whole tissue models of the C. elegans germ line where each cell in the model will execute a synthesized genetic network.