Meta-level Control in Multi-Agent Systems.

Abstract Sophisticated agents operating in open environments must make decisions that efficiently trade off the use of their limited resources between dynamic deliberative actions and domain actions? This is the meta-level control problem for agents operating in resource-bounded multi-agent environments. Control activities involve decisions on when to invoke and the amount to effort to put into scheduling and coordination of domain activities. The focus of this paper is how to make effective meta-level control decisions. We show that meta-level control with bounded computational overhead allows complex agents to solve problems more efficiently than current approaches in dynamic open multi-agent environments. The meta-level control approach that we present is based on the decision-theoretic use of an abstract representation of the agent state. This abstraction concisely captures critical information necessary for decision making while bounding the cost of meta-level control and is appropriate for use in automatically learning the meta-level control policies

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

Mission-Phasing Techniques for Constrained Agents in Stochastic Environments.

Resource constraints restrict the set of actions that an agent can take, such that the agent might not be able to perform all its desired tasks. Computational time limitations restrict the number of states that an agent can model and reason over, such that the agent might not be able to formulate a policy that can respond to all possible eventualities. This work argues that, in either situation, one effective way of improving the agent's performance is to adopt a phasing strategy. Resource-constrained agents can choose to reconfigure resources and switch action sets for handling upcoming events better when moving from phase to phase; time-limited agents can choose to focus computation on high-value phases and to exploit additional computation time during the execution of earlier phases to improve solutions for future phases. This dissertation consists of two parts, corresponding to the aforementioned resource constraints and computational time limitations. The first part of the dissertation focuses on the development of automated resource-driven mission-phasing techniques for agents operating in resource-constrained environments. We designed a suite of algorithms which not only can find solutions to optimize the use of predefined phase-switching points, but can also automatically determine where to establish such points, accounting for the cost of creating them, in complex stochastic environments. By formulating the coupled problems of mission decomposition, resource configuration, and policy formulation into a single compact mathematical formulation, the presented algorithms can effectively exploit problem structure and often considerably reduce computational cost for finding exact solutions. The second part of this dissertation is the design of computation-driven mission-phasing techniques for time-critical systems. We developed a new deliberation scheduling approach, which can simultaneously solve the coupled problems of deciding both when to deliberate given its cost, and which phase decision procedures to execute during deliberation intervals. Meanwhile, we designed a heuristic search method to effectively utilize the allocated time within each phase. As illustrated in experimental results, the computation-driven mission-phasing techniques, which extend problem decomposition techniques with the across-phase deliberation scheduling and inner-phase heuristic search methods mentioned above, can help an agent generate a better policy within time limit.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60650/1/jianhuiw_1.pd