4,449 research outputs found
Linking pattern to process in cultural evolution: explaining material culture diversity among the Northern Khanty of Northwest Siberia
Book description: This volume offers an integrative approach to the application of evolutionary theory in studies of cultural transmission and social evolution and reveals the enormous range of ways in which Darwinian ideas can lead to productive empirical research, the touchstone of any worthwhile theoretical perspective. While many recent works on cultural evolution adopt a specific theoretical framework, such as dual inheritance theory or human behavioral ecology, Pattern and Process in Cultural Evolution emphasizes empirical analysis and includes authors who employ a range of backgrounds and methods to address aspects of culture from an evolutionary perspective. Editor Stephen Shennan has assembled archaeologists, evolutionary theorists, and ethnographers, whose essays cover a broad range of time periods, localities, cultural groups, and artifacts
Human-Machine Collaborative Optimization via Apprenticeship Scheduling
Coordinating agents to complete a set of tasks with intercoupled temporal and
resource constraints is computationally challenging, yet human domain experts
can solve these difficult scheduling problems using paradigms learned through
years of apprenticeship. A process for manually codifying this domain knowledge
within a computational framework is necessary to scale beyond the
``single-expert, single-trainee" apprenticeship model. However, human domain
experts often have difficulty describing their decision-making processes,
causing the codification of this knowledge to become laborious. We propose a
new approach for capturing domain-expert heuristics through a pairwise ranking
formulation. Our approach is model-free and does not require enumerating or
iterating through a large state space. We empirically demonstrate that this
approach accurately learns multifaceted heuristics on a synthetic data set
incorporating job-shop scheduling and vehicle routing problems, as well as on
two real-world data sets consisting of demonstrations of experts solving a
weapon-to-target assignment problem and a hospital resource allocation problem.
We also demonstrate that policies learned from human scheduling demonstration
via apprenticeship learning can substantially improve the efficiency of a
branch-and-bound search for an optimal schedule. We employ this human-machine
collaborative optimization technique on a variant of the weapon-to-target
assignment problem. We demonstrate that this technique generates solutions
substantially superior to those produced by human domain experts at a rate up
to 9.5 times faster than an optimization approach and can be applied to
optimally solve problems twice as complex as those solved by a human
demonstrator.Comment: Portions of this paper were published in the Proceedings of the
International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and
in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper
consists of 50 pages with 11 figures and 4 table
Toward supervised reinforcement learning with partial states for social HRI
Social interacting is a complex task for which machine learning holds particular promise. However, as no sufficiently accurate simulator of human interactions exists today, the learning of social interaction strategies has to happen online in the real world. Actions executed by the robot impact on humans, and as such have to be carefully selected, making it impossible to rely on random exploration. Additionally, no clear reward function exists for social interactions. This implies that traditional approaches used for Reinforcement Learning cannot be directly applied for learning how to interact with the social world. As such we argue that robots will profit from human expertise and guidance to learn social interactions. However, as the quantity of input a human can provide is limited, new methods have to be designed to use human input more efficiently. In this paper we describe a setup in which we combine a framework called Supervised Progressively Autonomous Robot Competencies (SPARC), which allows safer online learning with Reinforcement Learning, with the use of partial states rather than full states to accelerate generalisation and obtain a usable action policy more quickly
Criminal intent or cognitive dissonance: how does student self plagiarism fit into academic integrity?
The discourse of plagiarism is speckled with punitive terms not out of place in a police officer's notes: detection, prevention, misconduct, rules, regulations, conventions, transgression, consequences, deter, trap, etc. This crime and punishment paradigm tends to be the norm in academic settings. The learning and teaching paradigm assumes that students are not filled with criminal intent, but rather are confused by the novel academic culture and its values. The discourse of learning and teaching includes: development, guidance, acknowledge, scholarly practice, communicate, familiarity, culture. Depending on the paradigm adopted, universities, teachers, and students will either focus on policies, punishments, and ways to cheat the system or on program design, assessments, and assimilating the values of academia. Self plagiarism is a pivotal issue that polarises these two paradigms. Viewed from a crime and punishment paradigm, self plagiarism is an intentional act of evading the required workload for a course by re-using previous work. Within a learning and teaching paradigm, self plagiarism is an oxymoron. We would like to explore the differences between these two paradigms by using self plagiarism as a focal point
Who Needs The Teacher?
The purpose of the present paper is to establish, define, and develop a high-level architecture for Systems of Learning from the viewpoint of interdependencies between the processes of self-organization and collective intelligence. The role of the Teacher/Manager therefore changes to the collective learning and intelligence taking the central stage. The paper also develops and justifies several views within the stated viewpoint serving better understanding of the relationship between these processes and offering a common ground for the discussion and development of various implementation scenarios
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