Refutaciones y la Lógica de la Práctica

When arguments are refuted in mathematics classrooms, the ways in which they are refuted can reveal something about the logic of practice evolving in the classroom, as well as about the epistemology that guides the teachers’ teaching. We provide four examples that illustrate refutations related to the logic of practice, in which sufficiency and relevance are grounds for refutation, as opposed to falsehood.Cuando se refutan argumentos en el aula, las maneras en que se refutan pueden revelar algo acerca del desarrollo de la lógica de la práctica en el aula, así como de la epistemología que guía la enseñanza. Presentamos cuatro ejemplos que ilustran refutaciones relacionadas con la lógica de la práctica, en los que la suficiencia y pertinencia, y no la falsedad, son los motivos de refutación

The Animal-AI Olympics

The past decade has seen great progress in artificial intelligence (AI). Machines can now categorize and generate images, make complex physical and social inferences, and reach, or exceed, human performance in many games. However, the long-term goal of recreating human-like general intelligence remains out of reach and some argue that a radical change in approach is needed

Refutations and the logic of practice

When arguments are refuted in mathematics classrooms, the ways in which they are refuted can reveal something about the logic of practice evolving in the classroom, as well as about the epistemology that guides the teachers’ teaching. We provide four examples that illustrate refutations related to the logic of practice, in which sufficiency and relevance are grounds for refutation, as opposed to falsehood. Refutaciones y la lógica de la práctica Cuando los argumentos son refutados en las aulas de matemáticas, las maneras en que estos son refutados pueden revelar algo acerca del desarrollo de la lógica de la práctica en el aula, así como de la epistemología que guía la enseñanza. Presentamos cuatro ejemplos que ilustran refutaciones relacionadas con la lógica de la práctica, en los que la suficiencia y pertinencia y no la falsedad son los motivos de refutación.Handle: http://hdl.handle.net/10481/1601

The Societal Implications of Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) is an avenue of research in Artificial Intelligence (AI) that has received increasing attention within the research community in recent years, and is beginning to show potential for real-world application. DRL is one of the most promising routes towards developing more autonomous AI systems that interact with and take actions in complex real-world environments, and can more flexibly solve a range of problems for which we may not be able to precisely specify a correct ‘answer’. This could have substantial implications for people’s lives: for example by speeding up automation in various sectors, changing the nature and potential harms of online influence, or introducing new safety risks in physical infrastructure. In this paper, we review recent progress in DRL, discuss how this may introduce novel and pressing issues for society, ethics, and governance, and highlight important avenues for future research to better understand DRL’s societal implications.&#x0D; &#x0D; &#x0D; &#x0D; This article appears in the special track on AI and Society.&#x0D; &#x0D; &#x0D; </jats:p

Refutations and the logic of practice

When arguments are refuted in mathematics classrooms, the ways in which they are refuted can reveal something about the logic of practice evolving in the classroom, as well as about the epistemology that guides the teachers’ teaching. We provide four examples that illustrate refutations related to the logic of practice, in which sufficiency and relevance are grounds for refutation, as opposed to falsehood. Refutaciones y la lógica de la práctica Cuando los argumentos son refutados en las aulas de matemáticas, las maneras en que estos son refutados pueden revelar algo acerca del desarrollo de la lógica de la práctica en el aula, así como de la epistemología que guía la enseñanza. Presentamos cuatro ejemplos que ilustran refutaciones relacionadas con la lógica de la práctica, en los que la suficiencia y pertinencia y no la falsedad son los motivos de refutación.Handle: http://hdl.handle.net/10481/1601

ADP an Agent Decomposition Planner CoDMAP 2015

ADP (an Agent Decomposition-based Planner) is de-signed to deduce agent decompositions from standard PDDL-encoded planning problems and then to exploit such found decompositions to generate a heuristic used for efficient planning. The decomposition process parti-tions the problem into an environment and a number of agents which act on and influence the environment, but can not (directly) effect each other. The heuristic calcu-lation is an adaptation of the FF relaxation heuristic to incorporate multiagent information. Relaxed planning graphs are only ever generated for single-agent sub-problems. However, when cooperation is necessary, an agent’s starting state may include facts added by others