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

Complex Interactions between Multiple Goal Operations in Agent Goal Management

A significant issue in cognitive systems research is to make an agent formulate and manage its own goals. Some cognitive scientists have implemented several goal operations to support this issue, but no one has implemented more than a couple of goal operations within a single agent. One of the reasons for this limitation is the lack of knowledge about how various goals operations interact with one another. This thesis addresses this knowledge gap by implementing multiple-goal operations, including goal formulation, goal change, goal selection, and designing an algorithm to manage any positive or negative interaction between them. These are integrated with a cognitive architecture called MIDCA and applied in five different test domains. We will compare and contrast the architecture\u27s performance with intelligent interaction management with a randomized linearization of goal operations

Complex Interactions between Multiple Goal Operations in Agent Goal Management

A significant issue in cognitive systems research is to make an agent formulate and manage its own goals. Some cognitive scientists have implemented several goal operations to support this issue, but no one has implemented more than a couple of goal operations within a single agent. One of the reasons for this limitation is the lack of knowledge about how various goals operations interact with one another. This thesis addresses this knowledge gap by implementing multiple-goal operations, including goal formulation, goal change, goal selection, and designing an algorithm to manage any positive or negative interaction between them. These are integrated with a cognitive architecture called MIDCA and applied in five different test domains. We will compare and contrast the architecture\u27s performance with intelligent interaction management with a randomized linearization of goal operations

Self Monitoring Goal Driven Autonomy Agents

The growing abundance of autonomous systems is driving the need for robust performance. Most current systems are not fully autonomous and often fail when placed in real environments. Via self-monitoring, agents can identify when their own, or externally given, boundaries are violated, thereby increasing their performance and reliability. Specifically, self-monitoring is the identification of unexpected situations that either (1) prohibit the agent from reaching its goal(s) or (2) result in the agent acting outside of its boundaries. Increasingly complex and open environments warrant the use of such robust autonomy (e.g., self-driving cars, delivery drones, and all types of future digital and physical assistants). The techniques presented herein advance the current state of the art in self-monitoring, demonstrating improved performance in a variety of challenging domains. In the aforementioned domains, there is an inability to plan for all possible situations. In many cases all aspects of a domain are not known beforehand, and, even if they were, the cost of encoding them is high. Self-monitoring agents are able to identify and then respond to previously unexpected situations, or never-before-encountered situations. When dealing with unknown situations, one must start with what is expected behavior and use that to derive unexpected behavior. The representation of expectations will vary among domains; in a real-time strategy game like Starcraft, it could be logically inferred concepts; in a mars rover domain, it could be an accumulation of actions\u27 effects. Nonetheless, explicit expectations are necessary to identify the unexpected. This thesis lays the foundation for self-monitoring in goal driven autonomy agents in both rich and expressive domains and in partially observable domains. We introduce multiple techniques for handling such environments. We show how inferred expectations are needed to enable high level planning in real-time strategy games. We show how a hierarchical structure of Goal-driven Autonomy (GDA) enables agents to operate within large state spaces. Within Hierarchical Task Network planning, we show how informed expectations identify states that are likely to prevent an agent from reaching its goals in dynamic domains. Finally, we give a model of expectations for self-monitoring at the meta-cognitive level, and empirical results of agents equipped with and without metacognitive expectations

Goal reasoning for autonomous agents using automated planning

Mención Internacional en el título de doctorAutomated planning deals with the task of finding a sequence of actions, namely a plan, which achieves a goal from a given initial state. Most planning research consider goals are provided by a external user, and agents just have to find a plan to achieve them. However, there exist many real world domains where agents should not only reason about their actions but also about their goals, generating new ones or changing them according to the perceived environment. In this thesis we aim at broadening the goal reasoning capabilities of planningbased agents, both when acting in isolation and when operating in the same environment as other agents. In single-agent settings, we firstly explore a special type of planning tasks where we aim at discovering states that fulfill certain cost-based requirements with respect to a given set of goals. By computing these states, agents are able to solve interesting tasks such as find escape plans that move agents in to safe places, hide their true goal to a potential observer, or anticipate dynamically arriving goals. We also show how learning the environment’s dynamics may help agents to solve some of these tasks. Experimental results show that these states can be quickly found in practice, making agents able to solve new planning tasks and helping them in solving some existing ones. In multi-agent settings, we study the automated generation of goals based on other agents’ behavior. We focus on competitive scenarios, where we are interested in computing counterplans that prevent opponents from achieving their goals. We frame these tasks as counterplanning, providing theoretical properties of the counterplans that solve them. We also show how agents can benefit from computing some of the states we propose in the single-agent setting to anticipate their opponent’s movements, thus increasing the odds of blocking them. Experimental results show how counterplans can be found in different environments ranging from competitive planning domains to real-time strategy games.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidenta: Eva Onaindía de la Rivaherrera.- Secretario: Ángel García Olaya.- Vocal: Mark Robert

Relational reinforcement learning for planning with exogenous effects

Probabilistic planners have improved recently to the point that they can solve difficult tasks with complex and expressive models. In contrast, learners cannot tackle yet the expressive models that planners do, which forces complex models to be mostly handcrafted. We propose a new learning approach that can learn relational probabilistic models with both action effects and exogenous effects. The proposed learning approach combines a multi-valued variant of inductive logic programming for the generation of candidate models, with an optimization method to select the best set of planning operators to model a problem. We also show how to combine this learner with reinforcement learning algorithms to solve complete problems. Finally, experimental validation is provided that shows improvements over previous work in both simulation and a robotic task. The robotic task involves a dynamic scenario with several agents where a manipulator robot has to clear the tableware on a table. We show that the exogenous effects learned by our approach allowed the robot to clear the table in a more efficient way.Peer ReviewedPostprint (published version

Goal Reasoning: Papers from the ACS Workshop

This technical report contains the 14 accepted papers presented at the Workshop on Goal Reasoning, which was held as part of the 2015 Conference on Advances in Cognitive Systems (ACS-15) in Atlanta, Georgia on 28 May 2015. This is the fourth in a series of workshops related to this topic, the first of which was the AAAI-10 Workshop on Goal-Directed Autonomy; the second was the Self-Motivated Agents (SeMoA) Workshop, held at Lehigh University in November 2012; and the third was the Goal Reasoning Workshop at ACS-13 in Baltimore, Maryland in December 2013

Goal Reasoning: Papers from the ACS workshop

This technical report contains the 11 accepted papers presented at the Workshop on Goal Reasoning, which was held as part of the 2013 Conference on Advances in Cognitive Systems (ACS-13) in Baltimore, Maryland on 14 December 2013. This is the third in a series of workshops related to this topic, the first of which was the AAAI-10 Workshop on Goal-Directed Autonomy while the second was the Self-Motivated Agents (SeMoA) Workshop, held at Lehigh University in November 2012. Our objective for holding this meeting was to encourage researchers to share information on the study, development, integration, evaluation, and application of techniques related to goal reasoning, which concerns the ability of an intelligent agent to reason about, formulate, select, and manage its goals/objectives. Goal reasoning differs from frameworks in which agents are told what goals to achieve, and possibly how goals can be decomposed into subgoals, but not how to dynamically and autonomously decide what goals they should pursue. This constraint can be limiting for agents that solve tasks in complex environments when it is not feasible to manually engineer/encode complete knowledge of what goal(s) should be pursued for every conceivable state. Yet, in such environments, states can be reached in which actions can fail, opportunities can arise, and events can otherwise take place that strongly motivate changing the goal(s) that the agent is currently trying to achieve. This topic is not new; researchers in several areas have studied goal reasoning (e.g., in the context of cognitive architectures, automated planning, game AI, and robotics). However, it has infrequently been the focus of intensive study, and (to our knowledge) no other series of meetings has focused specifically on goal reasoning. As shown in these papers, providing an agent with the ability to reason about its goals can increase performance measures for some tasks. Recent advances in hardware and software platforms (involving the availability of interesting/complex simulators or databases) have increasingly permitted the application of intelligent agents to tasks that involve partially observable and dynamically-updated states (e.g., due to unpredictable exogenous events), stochastic actions, multiple (cooperating, neutral, or adversarial) agents, and other complexities. Thus, this is an appropriate time to foster dialogue among researchers with interests in goal reasoning. Research on goal reasoning is still in its early stages; no mature application of it yet exists (e.g., for controlling autonomous unmanned vehicles or in a deployed decision aid). However, it appears to have a bright future. For example, leaders in the automated planning community have specifically acknowledged that goal reasoning has a prominent role among intelligent agents that act on their own plans, and it is gathering increasing attention from roboticists and cognitive systems researchers. In addition to a survey, the papers in this workshop relate to, among other topics, cognitive architectures and models, environment modeling, game AI, machine learning, meta-reasoning, planning, selfmotivated systems, simulation, and vehicle control. The authors discuss a wide range of issues pertaining to goal reasoning, including representations and reasoning methods for dynamically revising goal priorities. We hope that readers will find that this theme for enhancing agent autonomy to be appealing and relevant to their own interests, and that these papers will spur further investigations on this important yet (mostly) understudied topic

Learning Unknown Event Models

Agents with incomplete environment models are likely to be surprised, and this represents an opportunity to learn. We investigate approaches for situated agents to detect surprises, discriminate among different forms of surprise, and hypothesize new models for the unknown events that surprised them. We instantiate these approaches in a new goal reasoning agent (named FoolMeTwice), investigate its performance in simulation studies, and report that it produces plans with significantly reduced execution cost in comparison to not learning models for surprising events