Mixed Logical Inference and Probabilistic Planning for Robots in Unreliable Worlds

Abstract—Deployment of robots in practical domains poses key knowledge representation and reasoning challenges. Robots need to represent and reason with incomplete domain knowl-edge, acquiring and using sensor inputs based on need and availability. This paper presents an architecture that exploits the complementary strengths of declarative programming and probabilistic graphical models as a step towards addressing these challenges. Answer Set Prolog (ASP), a declarative language, is used to represent, and perform inference with, incomplete domain knowledge, including default information that holds in all but a few exceptional situations. A hierarchy of partially observable Markov decision processes (POMDPs) probabilistically models the uncertainty in sensor input processing and navigation. Non-monotonic logical inference in ASP is used to generate a multi-nomial prior for probabilistic state estimation with the hierarchy of POMDPs. It is also used with historical data to construct a Beta (meta) density model of priors for metareasoning and early termination of trials when appropriate. Robots equipped with this architecture automatically tailor sensor input processing and navigation to tasks at hand, revising existing knowledge using information extracted from sensor inputs. The architecture is empirically evaluated in simulation and on a mobile robot visually localizing objects in indoor domains. I

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

Metareasoning in Real-time Heuristic Search

In real-time heuristic search, a search agent must simultaneously find and execute a solution to a search problem with a strict time bound on the duration between action emissions and thus the duration of search between said emissions. Because these searches are required to emit actions before their desirability can be guaranteed, resulting solutions are typically sub-optimal in regards to total solution cost, but often superior in terms of the time from start of search to arrival at the goal. Metareasoning is the process of deliberating about the search process, including where to focus it and when to do it. Many modern real-time searches optimistically (and somewhat naively) direct the search agent to some state for which the estimated total solution cost appears low at the end of the most recent iteration of search. By employing metareasoning practices, we can hope to replace this optimism with an appropriate amount of skepticism, embracing the already sub-optimal nature of real-time search in such a way that known or expected sub-optimal actions are taken in an effort to direct the agent more effectively in the future. In this thesis we pursue this research direction by presenting and analyzing previous search algorithms and then proposing several metareasoning approaches which show promise in the form of both empirical results and rationalized intuition for minimizing the expected time until arrival at the goal. We evaluate the performance of these approaches thoroughly in order to highlight their strengths and their failings. Finally, we present a number of issues in metareasoning in real-time search which came to light in researching this topic, with the intent of guiding future research along promising avenues

Metareasoning for Planning and Execution in Autonomous Systems

Metareasoning is the process by which an autonomous system optimizes, specifically monitors and controls, its own planning and execution processes in order to operate more effectively in its environment. As autonomous systems rapidly grow in sophistication and autonomy, the need for metareasoning has become critical for efficient and reliable operation in noisy, stochastic, unstructured domains for long periods of time. This is due to the uncertainty over the limitations of their reasoning capabilities and the range of their potential circumstances. However, despite considerable progress in metareasoning as a whole over the last thirty years, work on metareasoning for planning relies on several assumptions that diminish its accuracy and practical utility in autonomous systems that operate in the real world while work on metareasoning for execution has not seen much attention yet. This dissertation therefore proposes more effective metareasoning for planning while expanding the scope of metareasoning to execution to improve the efficiency of planning and the reliability of execution in autonomous systems. In particular, we offer a two-pronged framework that introduces metareasoning for efficient planning and reliable execution in autonomous systems. We begin by proposing two forms of metareasoning for efficient planning: (1) a method that determines when to interrupt an anytime algorithm and act on the current solution by using online performance prediction and (2) a method that tunes the hyperparameters of the anytime algorithm at runtime by using deep reinforcement learning. We then propose two forms of metareasoning for reliable execution: (3) a method that recovers from exceptions that can be encountered during operation by using belief space planning and (4) a method that maintains and restores safety during operation by using probabilistic planning

Mixing non-monotonic logical reasoning and probabilistic planning for robots

This paper describes an architecture that com-bines the complementary strengths of probabilistic graphical models and declarative programming to represent and reason with qualitative and quantita-tive descriptions of domain knowledge and uncer-tainty. An action language is used for the architec-ture’s low-level (LL) and high-level (HL) system descriptions, and the HL definition of recorded his-tory is expanded to allow prioritized defaults. For any given objective, each action in the plan created in the HL using non-monotonic logical reasoning is executed probabilistically in the LL, refining the HL description to identify the relevant sorts, fluents and actions, and adding the corresponding action outcomes to the HL history. The HL and LL do-main representations are translated into an Answer Set Prolog (ASP) program and a partially observ-able Markov decision process (POMDP) respec-tively. ASP-based inference provides a multino-mial prior for POMDP state estimation, and pop-ulates a Beta density of priors for metareasoning and early termination. Robots equipped with this architecture reason with violation of defaults, noisy observations and unreliable actions in complex do-mains. The architecture is evaluated in simulation and on a mobile robot moving target objects to de-sired locations in an office domain.

Metareasoning about propagators for constraint satisfaction

Given the breadth of constraint satisfaction problems (CSPs) and the wide variety of CSP solvers, it is often very difficult to determine a priori which solving method is best suited to a problem. This work explores the use of machine learning to predict which solving method will be most effective for a given problem. We use four different problem sets to determine the CSP attributes that can be used to determine which solving method should be applied. After choosing an appropriate set of attributes, we determine how well j48 decision trees can predict which solving method to apply. Furthermore, we take a cost sensitive approach such that problem instances where there is a great difference in runtime between algorithms are emphasized. We also attempt to use information gained on one class of problems to inform decisions about a second class of problems. Finally, we show that the additional costs of deciding which method to apply are outweighed by the time savings compared to applying the same solving method to all problem instances

Principles for Consciousness in Integrated Cognitive Control

In this article we will argue that given certain conditions for the evolution of bi- \ud ological controllers, these will necessarily evolve in the direction of incorporating \ud consciousness capabilities. We will also see what are the necessary mechanics for \ud the provision of these capabilities and extrapolate this vision to the world of artifi- \ud cial systems postulating seven design principles for conscious systems. This article \ud was published in the journal Neural Networks special issue on brain and conscious- \ud ness

Modelling the Developing Mind: From Structure to Change

This paper presents a theory of cognitive change. The theory assumes that the fundamental causes of cognitive change reside in the architecture of mind. Thus, the architecture of mind as specified by the theory is described first. It is assumed that the mind is a three-level universe involving (1) a processing system that constrains processing potentials, (2) a set of specialized capacity systems that guide understanding of different reality and knowledge domains, and (3) a hypecognitive system that monitors and controls the functioning of all other systems. The paper then specifies the types of change that may occur in cognitive development (changes within the levels of mind, changes in the relations between structures across levels, changes in the efficiency of a structure) and a series of general (e.g., metarepresentation) and more specific mechanisms (e.g., bridging, interweaving, and fusion) that bring the changes about. It is argued that different types of change require different mechanisms. Finally, a general model of the nature of cognitive development is offered. The relations between the theory proposed in the paper and other theories and research in cognitive development and cognitive neuroscience is discussed throughout the paper