The Logic of Joint Ability in Two-Player Tacit Games

Logics of joint strategic ability have recently received attention, with arguably the most influential being those in a family that includes Coalition Logic (CL) and Alternating-time Temporal Logic (ATL). Notably, both CL and ATL bypass the epistemic issues that underpin Schelling-type coordination problems, by apparently relying on the meta-level assumption of (perfectly reliable) communication between cooperating rational agents. Yet such epistemic issues arise naturally in settings relevant to ATL and CL: these logics are standardly interpreted on structures where agents move simultaneously, opening the possibility that an agent cannot foresee the concurrent choices of other agents. In this paper we introduce a variant of CL we call Two-Player Strategic Coordination Logic (SCL2). The key novelty of this framework is an operator for capturing coalitional ability when the cooperating agents cannot share strategic information. We identify significant differences in the expressive power and validities of SCL2 and CL2, and present a sound and complete axiomatization for SCL2. We briefly address conceptual challenges when shifting attention to games with more than two players and stronger notions of rationality

Theoretical models of the role of visualisation in learning formal reasoning

Although there is empirical evidence that visualisation tools can help students to learn formal subjects such as logic, and although particular strategies and conceptual difficulties have been identified, it has so far proved difficult to provide a general model of learning in this context that accounts for these findings in a systematic way. In this paper, four attempts at explaining the relative difficulty of formal concepts and the role of visualisation in this learning process are presented. These explanations draw on several existing theories, including Vygotsky's Zone of Proximal Development, Green's Cognitive Dimensions, the Popper-Campbell model of conjectural learning, and cognitive complexity. The paper concludes with a comparison of the utility and applicability of the different models. It is also accompanied by a reflexive commentary[0] (linked to this paper as a hypertext) that examines the ways in which theory has been used within these arguments, and which attempts to relate these uses to the wider context of learning technology research

Towards an Intelligent Tutor for Mathematical Proofs

Computer-supported learning is an increasingly important form of study since it allows for independent learning and individualized instruction. In this paper, we discuss a novel approach to developing an intelligent tutoring system for teaching textbook-style mathematical proofs. We characterize the particularities of the domain and discuss common ITS design models. Our approach is motivated by phenomena found in a corpus of tutorial dialogs that were collected in a Wizard-of-Oz experiment. We show how an intelligent tutor for textbook-style mathematical proofs can be built on top of an adapted assertion-level proof assistant by reusing representations and proof search strategies originally developed for automated and interactive theorem proving. The resulting prototype was successfully evaluated on a corpus of tutorial dialogs and yields good results.Comment: In Proceedings THedu'11, arXiv:1202.453

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

Ethical judgment and radical business changes: the role of entrepreneurial perspicacity

This study examines the implications of practical reason for entrepreneurial activities. Our study is based on Thomas Aquinas’ interpretation of such virtue, with a particular focus on the partition of practical reason in potential parts such as synesis, or common sense, and gnome, or perspicacity. Since entrepreneurial acts and actions deal with extremely uncertain situations, we argue that only this perspicacity, as the ability of correctly judging in exceptional cases, has the power to find wisdom under such blurred conditions. Perspicacity frees entrepreneurs from their cognitive schemata rendering them able to be truly entrepreneurial. Based on this vision and thanks to a semantic analysis of the meaning of the Greek word gnome, we construct an interpretative model for entrepreneurial judgment composed of three dimensions, specifically, knowledge-cognitive, external-affective and personal-reflective. The model highlights how a ‘successful’ entrepreneurial judgment is also such from a holistic point of view

Reasoning about Knowledge and Strategies under Hierarchical Information

Two distinct semantics have been considered for knowledge in the context of strategic reasoning, depending on whether players know each other's strategy or not. The problem of distributed synthesis for epistemic temporal specifications is known to be undecidable for the latter semantics, already on systems with hierarchical information. However, for the other, uninformed semantics, the problem is decidable on such systems. In this work we generalise this result by introducing an epistemic extension of Strategy Logic with imperfect information. The semantics of knowledge operators is uninformed, and captures agents that can change observation power when they change strategies. We solve the model-checking problem on a class of "hierarchical instances", which provides a solution to a vast class of strategic problems with epistemic temporal specifications on hierarchical systems, such as distributed synthesis or rational synthesis

Making the Public Case for Child Abuse and Neglect Prevention: A FrameWorks Message Memo

The goal of this work is to evaluate the existing body of research available to Prevent Child Abuse America against the findings that emerge from new research, and to identify promising ways to reframe these issues in ways that engage people in prevention, motivate them to prioritize proven policies and programs, and overcome existing mental roadblocks. To that end, this Memo attempts to describe the translation process necessary to engage the public in solutions by identifying specific practices that research suggests would advance public understanding as well as those that are likely to impede it.This research analysis is part of New FrameWorks Research on Child Abuse and Neglect Prevention. Please visit our website for more information