Organization, Evolution, Cognition and Dynamic Capabilities

Using insights from 'embodied cognition' and a resulting 'cognitive theory of the firm', I aim to contribute to the further development of evolutionary theory of organizations, in the specification of organizations as 'interactors' that carry organizational competencies as 'replicators', within industries as 'populations'.Especially, I analyze how, if at all, 'dynamic capabilities' can be fitted into evolutionary theory.I propose that the prime purpose of an organization is to serve as a cognitive 'focusing device'.Here, cognition has a wide meaning, including perception, interpretation, sense making, and value judgements.I analyse how this yields organizations as cohesive wholes, and differences within and between industries.I propose the following sources of variation: replication in communication, novel combinations of existing knowledge, and a path of discovery by which exploitation leads to exploration. These yield a proposal for dynamic capabilities.I discuss in what sense, and to what extent these sources of variation are 'blind' , as postulated in evolutionary theory.organization;evolution;cognition;dynamic capabilities;learning;invention

Organization, Evolution, Cognition and Dynamic Capabilities

Using insights from ‘embodied cognition’ and a resulting ‘cognitive theory of the firm’, I aim to contribute to the further development of evolutionary theory of organizations, in the specification of organizations as ‘interactors’ that carry organizational competencies as ‘replicators’, within industries as ‘populations’. Especially, I analyze how, if at all, ‘dynamic capabilities’ can be fitted into evolutionary theory. I propose that the prime purpose of an organization is to serve as a cognitive ‘focusing device’. Here, cognition has a wide meaning, including perception, interpretation, sense making, and value judgements. I analyse how this yields organizations as cohesive wholes, and differences within and between industries. I propose the following sources of variation: replication in communication, novel combinations of existing knowledge, and a path of discovery by which exploitation leads to exploration. These yield a proposal for dynamic capabilities. I discuss in what sense, and to what extent these sources of variation are ‘blind’, as postulated in evolutionary theory.evolutionary economics;organization;cognition;dynamic capabilities

Learning relational models with human interaction for planning in robotics

Automated planning has proven to be useful to solve problems where an agent has to maximize a reward function by executing actions. As planners have been improved to salve more expressive and difficult problems, there is an increasing interest in using planning to improve efficiency in robotic tasks. However, planners rely on a domain model, which has to be either handcrafted or learned. Although learning domain models can be very costly, recent approaches provide generalization capabilities and integrate human feedback to reduce the amount of experiences required to learn. In this thesis we propase new methods that allow an agent with no previous knowledge to solve certain problems more efficiently by using task planning. First, we show how to apply probabilistic planning to improve robot performance in manipulation tasks (such as cleaning the dirt or clearing the tableware on a table). Planners obtain sequences of actions that get the best result in the long term, beating reactive strategies. Second, we introduce new reinforcement learning algorithms where the agent can actively request demonstrations from a teacher to learn new actions and speed up the learning process. In particular, we propase an algorithm that allows the user to set the mínimum quality to be achieved, where a better quality also implies that a larger number of demonstrations will be requested . Moreover, the learned model is analyzed to extract the unlearned or problematic parts of the model. This information allow the agent to provide guidance to the teacher when a demonstration is requested, and to avoid irrecoverable errors. Finally, a new domain model learner is introduced that, in addition to relational probabilistic action models, can also learn exogenous effects. This learner can be integrated with existing planners and reinforcement learning algorithms to salve a wide range of problems. In summary, we improve the use of learning and task planning to salve unknown tasks. The improvements allow an agent to obtain a larger benefit from planners, learn faster, balance the number of action executions and teacher demonstrations, avoid irrecoverable errors, interact with a teacher to solve difficult problems, and adapt to the behavior of other agents by learning their dynamics. All the proposed methods were compared with state-of-the-art approaches, and were also demonstrated in different scenarios, including challenging robotic tasks.La planificación automática ha probado ser de gran utilidad para resolver problemas en los que un agente tiene que ejecutar acciones para maximizar una función de recompensa. A medida que los planificadores han sido capaces de resolver problemas cada vez más complejos, ha habido un creciente interés por utilizar dichos planificadores para mejorar la eficiencia de tareas robóticas. Sin embargo, los planificadores requieren un modelo del dominio, el cual puede ser creado a mano o aprendido. Aunque aprender modelos automáticamente puede ser costoso, recientemente han aparecido métodos que permiten la interacción persona-máquina y generalizan el conocimiento para reducir la cantidad de experiencias requeridas para aprender. En esta tesis proponemos nuevos métodos que permiten a un agente sin conocimiento previo de la tarea resolver problemas de forma más eficiente mediante el uso de planificación automática. Comenzaremos mostrando cómo aplicar planificación probabilística para mejorar la eficiencia de robots en tareas de manipulación (como limpiar suciedad o recoger una mesa). Los planificadores son capaces de obtener las secuencias de acciones que producen los mejores resultados a largo plazo, superando a las estrategias reactivas. Por otro lado, presentamos nuevos algoritmos de aprendizaje por refuerzo en los que el agente puede solicitar demostraciones a un profesor. Dichas demostraciones permiten al agente acelerar el aprendizaje o aprender nuevas acciones. En particular, proponemos un algoritmo que permite al usuario establecer la mínima suma de recompensas que es aceptable obtener, donde una recompensa más alta implica que se requerirán más demostraciones. Además, el modelo aprendido será analizado para identificar qué partes están incompletas o son problemáticas. Esta información permitirá al agente evitar errores irrecuperables y también guiar al profesor cuando se solicite una demostración. Finalmente, se ha introducido un nuevo método de aprendizaje para modelos de dominios que, además de obtener modelos relacionales de acciones probabilísticas, también puede aprender efectos exógenos. Mostraremos cómo integrar este método en algoritmos de aprendizaje por refuerzo para poder abordar una mayor cantidad de problemas. En resumen, hemos mejorado el uso de técnicas de aprendizaje y planificación para resolver tareas desconocidas a priori. Estas mejoras permiten a un agente aprovechar mejor los planificadores, aprender más rápido, elegir entre reducir el número de acciones ejecutadas o el número de demostraciones solicitadas, evitar errores irrecuperables, interactuar con un profesor para resolver problemas complejos, y adaptarse al comportamiento de otros agentes aprendiendo sus dinámicas. Todos los métodos propuestos han sido comparados con trabajos del estado del arte, y han sido evaluados en distintos escenarios, incluyendo tareas robóticas

Learning Social Work, between classroom and professional practices: A relational based social work with undergraduate students

Theoretical, methodological, and ethical standards postulate that social work education requires a student-oriented process that encourages learners to deeply reflect on professional practice. It is recommended that professional practice is integrated into social work education in order to prepare learners holistically and adequately to respond to contemporary social problems. This paper draws to advance empirically based outcomes stemming from a reflective process of a student-oriented pedagogical approach that incorporates professional practice in the training of social work un dergraduate students in Portugal. The pedagogical processes are based on relational social work, participation and reflection on practice as essential for engaging students with the profession and transforming practice. In this pedagogical process, we demonstrate how important it is for students to reflect on what they observe in the fieldwork; in the sense of self-knowledge, ability to adapt to new challenges, and understanding and reflection on significant events of practice. The above deliberations are a result of a survey carried out among fifty-one students in a social work degree in Portugal, in the laboratory social work unit that socialised students with the profession. In the unit, for the first-time students were confronted with the professional reality through direct observation of social work practices. From the survey, it was realised that relational experiences of socialisation with the profession and the observation of relevant social situations aid the devel opment of personal and interpersonal skills and as well strengthen the vocation of students. We conclude that this learning and relational process is essential for students to transform themselves as individuals and reinforce their professional vocation.info:eu-repo/semantics/publishedVersio

Integrating Planning, Execution, and Learning to Improve Plan Execution

Algorithms for planning under uncertainty require accurate action models that explicitly capture the uncertainty of the environment. Unfortunately, obtaining these models is usually complex. In environments with uncertainty, actions may produce countless outcomes and hence, specifying them and their probability is a hard task. As a consequence, when implementing agents with planning capabilities, practitioners frequently opt for architectures that interleave classical planning and execution monitoring following a replanning when failure paradigm. Though this approach is more practical, it may produce fragile plans that need continuous replanning episodes or even worse, that result in execution dead-ends. In this paper, we propose a new architecture to relieve these shortcomings. The architecture is based on the integration of a relational learning component and the traditional planning and execution monitoring components. The new component allows the architecture to learn probabilistic rules of the success of actions from the execution of plans and to automatically upgrade the planning model with these rules. The upgraded models can be used by any classical planner that handles metric functions or, alternatively, by any probabilistic planner. This architecture proposal is designed to integrate off-the-shelf interchangeable planning and learning components so it can profit from the last advances in both fields without modifying the architecture.Publicad

What is missing in autonomous discovery: Open challenges for the community

Self-driving labs (SDLs) leverage combinations of artificial intelligence, automation, and advanced computing to accelerate scientific discovery. The promise of this field has given rise to a rich community of passionate scientists, engineers, and social scientists, as evidenced by the development of the Acceleration Consortium and recent Accelerate Conference. Despite its strengths, this rapidly developing field presents numerous opportunities for growth, challenges to overcome, and potential risks of which to remain aware. This community perspective builds on a discourse instantiated during the first Accelerate Conference, and looks to the future of self-driving labs with a tempered optimism. Incorporating input from academia, government, and industry, we briefly describe the current status of self-driving labs, then turn our attention to barriers, opportunities, and a vision for what is possible. Our field is delivering solutions in technology and infrastructure, artificial intelligence and knowledge generation, and education and workforce development. In the spirit of community, we intend for this work to foster discussion and drive best practices as our field grows

A Review of Symbolic, Subsymbolic and Hybrid Methods for Sequential Decision Making

The field of Sequential Decision Making (SDM) provides tools for solving Sequential Decision Processes (SDPs), where an agent must make a series of decisions in order to complete a task or achieve a goal. Historically, two competing SDM paradigms have view for supremacy. Automated Planning (AP) proposes to solve SDPs by performing a reasoning process over a model of the world, often represented symbolically. Conversely, Reinforcement Learning (RL) proposes to learn the solution of the SDP from data, without a world model, and represent the learned knowledge subsymbolically. In the spirit of reconciliation, we provide a review of symbolic, subsymbolic and hybrid methods for SDM. We cover both methods for solving SDPs (e.g., AP, RL and techniques that learn to plan) and for learning aspects of their structure (e.g., world models, state invariants and landmarks). To the best of our knowledge, no other review in the field provides the same scope. As an additional contribution, we discuss what properties an ideal method for SDM should exhibit and argue that neurosymbolic AI is the current approach which most closely resembles this ideal method. Finally, we outline several proposals to advance the field of SDM via the integration of symbolic and subsymbolic AI

Learning in clinical practice: findings from CT, MRI and PACS

This thesis explores learning in clinical practice in the cases of CT, MRI and PACS in UK hospitals. It asks the questions of how and why certain evolutionary features of technology condition learning and change in medical contexts. Using an evolutionary perspective of cognitive and social aspects of technological change, this thesis explores the relationships between technology and organisational learning processes of intuition, interpretation, integration and institutionalisation. Technological regimes are manifested in routines, skills and artefacts, and dynamically evolve with knowledge accumulation processes at the individual, group and organisational levels. Technological change increases the uncertainty and complexity of organisational learning, making organisational outcomes partially unpredictable. Systemic and emergent properties of medical devices such as CT and MRI make learning context-specific and experimental. Negotiation processes between different social groups shape the role and function of an artefact in an organisational context. Technological systems connect artefacts to other parts of society, mediating values, velocity and directionality of change. Practice communities affect how organisations deal with this complexity and learn. These views are used to explore the accumulation of knowledge in clinical practices in CT, MRI and PACS. This thesis develops contextualised theory using a case-study approach to gather novel empirical data from over 40 interviews with clinical, technical, managerial and administrative staff in five NHS hospitals. It uses clinical practice (such as processes, procedures, tasks, rules, interpretations and routines) as a unit of analysis and CT, MRI and PACS technology areas as cases. Results are generalised to evolutionary aspects of technological learning and change provided by the framework, using processes for qualitative analysis such as ordering and coding. When analysed using an evolutionary perspective of technology, the findings in this thesis suggest that learning in clinical practice is diverse, cumulative and incremental, and shaped by complex processes of mediation, by issues such as disease complexity, values, external rules and choice restrictions from different regimes, and by interdisciplinary problem-solving in operational routines