Logic, self-awareness and self-improvement: The metacognitive loop and the problem of brittleness

This essay describes a general approach to building perturbation-tolerant autonomous systems, based on the conviction that artificial agents should be able notice when something is amiss, assess the anomaly, and guide a solution into place. We call this basic strategy of self-guided learning the metacognitive loop; it involves the system monitoring, reasoning about, and, when necessary, altering its own decision-making components. In this essay, we (a) argue that equipping agents with a metacognitive loop can help to overcome the brittleness problem, (b) detail the metacognitive loop and its relation to our ongoing work on time-sensitive commonsense reasoning, (c) describe specific, implemented systems whose perturbation tolerance was improved by adding a metacognitive loop, and (d) outline both short-term and long-term research agendas

Decisionmaking in practice: The dynamics of muddling through

An alternative to conventional models that treat decisions as open-loop independent choices is presented. The alterative model is based on observations of work situations such as healthcare, where decisionmaking is more typically a closed-loop, dynamic, problem-solving process. The article suggests five important distinctions between the processes assumed by conventional models and the reality of decisionmaking in practice. It is suggested that the logic of abduction in the form of an adaptive,muddling through process is more consistent with the realities of practice in domains such as healthcare.The practical implication is that the design goal should not be to improve consistency with normativemodels of rationality, but to tune the representations guiding the muddling process to increase functional perspicacity

Towards the Discovery of Learner Metacognition from Reflective Writing

Modern society demands renewed attention on the competencies required to best equip students for a dynamic and uncertain future. We present exploratory work based on the premise that metacognitive and reflective competencies are essential for this task. Bringing the concepts of metacognition and reflection together into a conceptual model within which we conceived of them as both a set of similar features, and as a spectrum ranging from the unconscious inner-self through to the conscious, external, social self. This model was used to guide exploratory computational analysis of 6,090 instances of reflective writing authored by undergraduate students. We found the conceptual model useful in informing the computational analysis, which in turn showed potential for automating the discovery of metacognitive activity in reflective writing, an approach that holds promise for the generation of formative feedback for students as they work towards developing core 21st century competencies

Toward Meta-level Control of Autonomous Agents

AbstractMetareasoning is an important capability for autonomous systems, particularly for those being deployed on long duration missions. An agent with increased self-observation and the ability to control itself in response to changing environments will be more capable in achieving its goals. This is essential for long-duration missions where system designers will not be able to, theoretically or practically, predict all possible problems that the agent may encounter. In this paper we describe preliminary work that integrates the metacognitive architecture MIDCA with an autonomous TREX agent, creating a more self-observable and adaptive agent

A validated ontology for meta-level control domain

The main objective of meta-level control is to decide what and how much reasoning to do instead of what actions to do. Meta-level control domain involves a large number of processes and actions with terminology that become confusing. For this reason, an ontology to describe the semantic relationships and hierarchical structure of terms related to metacognition is proposed. The ontology was developed based on definitions found in the literature. Experts validated the ontology using a survey. The validation result indicated that the design of an ontology based on the meta-level control domain allows reusing and sharing knowledge defining a common vocabulary

Clashes in the Infosphere, General Intelligence, and Metacognition: Final project report

Humans confront the unexpected every day, deal with it, and often learn from it. AI agents, on the other hand, are typically brittle—they tend to break down as soon as something happens for which their creators did not explicitly anticipate. The central focus of our research project is this problem of brittleness which may also be the single most important problem facing AI research. Our approach to brittleness is to model a common method that humans use to deal with the unexpected, namely to note occurrences of the unexpected (i.e., anomalies), to assess any problem signaled by the anomaly, and then to guide a response or solution that resolves it. The result is the Note-Assess-Guide procedure of what we call the Metacognitive Loop or MCL. To do this, we have implemented MCL-based systems that enable agents to help themselves; they must establish expectations and monitor them, note failed expectations, assess their causes, and then choose appropriate responses. Activities for this project have developed and refined a human-dialog agent and a robot navigation system to test the generality of this approach

Analysis of models and metacognitive architectures in intelligent systems

Recently Intelligent Systems (IS) have highly increased the autonomy of their decisions, this has been achieved by improving metacognitive skills. The term metacognition in Artifi cial Intelligence (AI) refers to the capability of IS to monitor and control their own learning processes. This paper describes different models used to address the implementation of metacognition in IS. Then, we present a comparative analysis among the different models of metacognition. As well as, a discussion about the following categories of analysis: types of metacognition architectural support of metacognition components, architectural cores and computational implementations

Anomaly Detection for Symbolic Representations

A fully autonomous agent recognizes new problems, explains what causes such problems, and generates its own goals to solve these problems. Our approach to this goal-driven model of autonomy uses a methodology called the Note-Assess-Guide procedure. It instantiates a monitoring process in which an agent notes an anomaly in the world, assesses the nature and cause of that anomaly, and guides appropriate modifications to behavior. This report describes a novel approach to the note phase of that procedure. A-distance, a sliding-window statistical distance metric, is applied to numerical vector representations of intermediate states from plans generated for two symbolic domains. Using these representations, the metric is able to detect anomalous world states caused by restricting the actions available to the planner

Metamodel for personalized adaptation of pedagogical strategies using metacognition in Intelligent Tutoring Systems

The modeling process of metacognitive functions in Intelligent Tutoring Systems (ITS) is a difficult and time-consuming task. In particular when the integration of several metacognitive components, such as self-regulation and metamemory is needed. Metacognition has been used in Artificial Intelligence (AI) to improve the performance of complex systems such as ITS. However the design ITS with metacognitive capabilities is a complex task due to the number and complexity of processes involved. The modeling process of ITS is in itself a difficult task and often requires experienced designers and programmers, even when using authoring tools. In particular the design of the pedagogical strategies for an ITS is complex and requires the interaction of a number of variables that define it as a dynamic process. This doctoral thesis presents a metamodel for the personalized adaptation of pedagogical strategies integrating metamemory and self-regulation in ITS. The metamodel called MPPSM (Metamodel of Personalized adaptation of Pedagogical Strategies using Metacognition in intelligent tutoring systems) was synthetized from the analysis of 40 metacognitive models and 45 ITS models that exist in the literature. MPPSMhas a conceptual architecture with four levels of modeling according to the standard Meta- Object Facility (MOF) of Model-Driven Architecture (MDA) methodology. MPPSM enables designers to have modeling tools in early stage of software development process to produce more robust ITS that are able to self-regulate their own reasoning and learning processes. In this sense, a concrete syntax composed of a graphic notation called M++ was defined in order to make the MPPSM metamodel more usable. M++ is a Domain-Specific Visual Language (DSVL) for modeling metacognition in ITS. M++ has approximately 20 tools for modeling metacognitive systems with introspective monitoring and meta-level control. MPPSM allows the generation of metacognitive models using M++ in a visual editor named MetaThink. In MPPSM-based models metacognitive components required for monitoring and executive control of the reasoning processes take place in each module of an ITS can be specified. MPPSM-based models represent the cycle of reasoning of an ITS about: (i) failures generated in its own reasoning tasks (e.g. self-regulation); and (ii) anomalies in events that occur in its Long-Term Memory (LTM) (e.g. metamemory). A prototype of ITS called FUNPRO was developed for the validation of the performance of metacognitive mechanism of MPPSM in the process of the personalization of pedagogical strategies regarding to the preferences and profiles of real students. FUNPRO uses self-regulation to monitor and control the processes of reasoning at object-level and metamemory for the adaptation to changes in the constraints of information retrieval tasks from LTM. The major contributions of this work are: (i) the MOF-based metamodel for the personalization of pedagogical strategies using computational metacognition in ITS; (ii) the M++ DSVL for modeling metacognition in ITS; and (iii) the ITS prototype called FUNPRO (FUNdamentos de PROgramación) that aims to provide personalized instruction in the subject of Introduction to Programming. The results given in the experimental tests demonstrate: (i) metacognitive models generated are consistent with the MPPSM metamodel; (ii) positive perceptions of users with respect to the proposed DSVL and it provide preliminary information concerning the quality of the concrete syntax of M++; (iii) in FUNPRO, multi-level pedagogical model enhanced with metacognition allows the dynamic adaptation of the pedagogical strategy according to the profile of each student.Doctorad

MIDCA: A Metacognitive, Integrated Dual-Cycle Architecture for Self-Regulated Autonomy

This report documents research performed under ONR grant N000141210172 for the period 1 June 2012 through 31 May 2013. The goals of this research are to provide a sound theoretical understanding of the role of metacognition in cognitive architectures and to demonstrate the underlying theory through implemented computational models. During the last year, the team has been integrating existing implemented systems to form an initial architectural structure that approximates the major functions of MIDCA. These include the SHOP2 hierarchical planning system and the Meta-AQUA integrated multistrategy learning system. We have also produced substantial progress on the data-driven track of the interpretation procedure. Last year’s work on using the A-distance metric for anomaly detection has been matured, and we have collected substantial observations used in empirical evaluation. Additionally we started implementation of a neural network to induce proto-type nodes for observed anomalies, and we are developing methods to prioritize explanations and responses that have proven effective with past anomalies in proto-type categories. The data are encouraging and the research community has reacted favorably. Several new publications support our claims herein