Logic Programming for Finding Models in the Logics of Knowledge and its Applications: A Case Study

The logics of knowledge are modal logics that have been shown to be effective in representing and reasoning about knowledge in multi-agent domains. Relatively few computational frameworks for dealing with computation of models and useful transformations in logics of knowledge (e.g., to support multi-agent planning with knowledge actions and degrees of visibility) have been proposed. This paper explores the use of logic programming (LP) to encode interesting forms of logics of knowledge and compute Kripke models. The LP modeling is expanded with useful operators on Kripke structures, to support multi-agent planning in the presence of both world-altering and knowledge actions. This results in the first ever implementation of a planner for this type of complex multi-agent domains.Comment: 16 pages, 1 figure, International Conference on Logic Programming 201

Knowledge-based Reasoning and Learning under Partial Observability in Ad Hoc Teamwork

Ad hoc teamwork refers to the problem of enabling an agent to collaborate with teammates without prior coordination. Data-driven methods represent the state of the art in ad hoc teamwork. They use a large labeled dataset of prior observations to model the behavior of other agent types and to determine the ad hoc agent's behavior. These methods are computationally expensive, lack transparency, and make it difficult to adapt to previously unseen changes, e.g., in team composition. Our recent work introduced an architecture that determined an ad hoc agent's behavior based on non-monotonic logical reasoning with prior commonsense domain knowledge and predictive models of other agents' behavior that were learned from limited examples. In this paper, we substantially expand the architecture's capabilities to support: (a) online selection, adaptation, and learning of the models that predict the other agents' behavior; and (b) collaboration with teammates in the presence of partial observability and limited communication. We illustrate and experimentally evaluate the capabilities of our architecture in two simulated multiagent benchmark domains for ad hoc teamwork: Fort Attack and Half Field Offense. We show that the performance of our architecture is comparable or better than state of the art data-driven baselines in both simple and complex scenarios, particularly in the presence of limited training data, partial observability, and changes in team composition.Comment: 17 pages, 3 Figure

Knowledge-based Reasoning and Learning under Partial Observability in Ad Hoc Teamwork

Ad hoc teamwork (AHT) refers to the problem of enabling an agent to collaborate with teammates without prior coordination. State of the art methods in AHT are data-driven, using a large labeled dataset of prior observations to model the behavior of other agent types and to determine the ad hoc agent’s behavior. These methods are computationally expensive, lack transparency, and make it difficult to adapt to previously unseen changes. Our recent work introduced an architecture that determined an ad hoc agent’s behavior based on non-monotonic logical reasoning with prior commonsense domain knowledge and models learned from limited examples to predict the behavior of other agents. This paper describes KAT, a knowledge-driven architecture for AHT that substantially expands our prior architecture’s capabilities to support: (a) online selection, adaptation, and learning of the behavior prediction models; and (b) collaboration with teammates in the presence of partial observability and limited communication. We illustrate and experimentally evaluate KAT’s capabilities in two simulated benchmark domains for multiagent collaboration: Fort Attack and Half Field Offense. We show that KAT’s performance is better than a purely knowledge-driven baseline, and comparable with or better than a state of the art data-driven baseline, particularly in the presence of limited training data, partial observability, and changes in team composition

Logic programming for deliberative robotic task planning

Over the last decade, the use of robots in production and daily life has increased. With increasingly complex tasks and interaction in different environments including humans, robots are required a higher level of autonomy for efficient deliberation. Task planning is a key element of deliberation. It combines elementary operations into a structured plan to satisfy a prescribed goal, given specifications on the robot and the environment. In this manuscript, we present a survey on recent advances in the application of logic programming to the problem of task planning. Logic programming offers several advantages compared to other approaches, including greater expressivity and interpretability which may aid in the development of safe and reliable robots. We analyze different planners and their suitability for specific robotic applications, based on expressivity in domain representation, computational efficiency and software implementation. In this way, we support the robotic designer in choosing the best tool for his application

Logic-based Technologies for Multi-agent Systems: A Systematic Literature Review

Precisely when the success of artificial intelligence (AI) sub-symbolic techniques makes them be identified with the whole AI by many non-computerscientists and non-technical media, symbolic approaches are getting more and more attention as those that could make AI amenable to human understanding. Given the recurring cycles in the AI history, we expect that a revamp of technologies often tagged as “classical AI” – in particular, logic-based ones will take place in the next few years. On the other hand, agents and multi-agent systems (MAS) have been at the core of the design of intelligent systems since their very beginning, and their long-term connection with logic-based technologies, which characterised their early days, might open new ways to engineer explainable intelligent systems. This is why understanding the current status of logic-based technologies for MAS is nowadays of paramount importance. Accordingly, this paper aims at providing a comprehensive view of those technologies by making them the subject of a systematic literature review (SLR). The resulting technologies are discussed and evaluated from two different perspectives: the MAS and the logic-based ones

REBA: A Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics

This paper describes an architecture for robots that combines the complementary strengths of probabilistic graphical models and declarative programming to represent and reason with logic-based and probabilistic descriptions of uncertainty and domain knowledge. An action language is extended to support non-boolean fluents and non-deterministic causal laws. This action language is used to describe tightly-coupled transition diagrams at two levels of granularity, with a fine-resolution transition diagram defined as a refinement of a coarse-resolution transition diagram of the domain. The coarse-resolution system description, and a history that includes (prioritized) defaults, are translated into an Answer Set Prolog (ASP) program. For any given goal, inference in the ASP program provides a plan of abstract actions. To implement each such abstract action, the robot automatically zooms to the part of the fine-resolution transition diagram relevant to this action. A probabilistic representation of the uncertainty in sensing and actuation is then included in this zoomed fine-resolution system description, and used to construct a partially observable Markov decision process (POMDP). The policy obtained by solving the POMDP is invoked repeatedly to implement the abstract action as a sequence of concrete actions, with the corresponding observations being recorded in the coarse-resolution history and used for subsequent reasoning. The architecture is evaluated in simulation and on a mobile robot moving objects in an indoor domain, to show that it supports reasoning with violation of defaults, noisy observations and unreliable actions, in complex domains.Comment: 72 pages, 14 figure

Towards VEsNA, a Framework for Managing Virtual Environments via Natural Language Agents

Automating a factory where robots are involved is neither trivial nor cheap. Engineering the factory automation process in such a way that return of interest is maximized and risk for workers and equipment is minimized, is hence of paramount importance. Simulation can be a game changer in this scenario but requires advanced programming skills that domain experts and industrial designers might not have. In this paper we present the preliminary design and implementation of a general-purpose framework for creating and exploiting Virtual Environments via Natural language Agents (VEsNA). VEsNA takes advantage of agent-based technologies and natural language processing to enhance the design of virtual environments. The natural language input provided to VEsNA is understood by a chatbot and passed to a cognitive intelligent agent that implements the logic behind displacing objects in the virtual environment. In the VEsNA vision, the intelligent agent will be able to reason on this displacement and on its compliance to legal and normative constraints. It will also be able to implement what-if analysis and case-based reasoning. Objects populating the virtual environment will include active objects and will populate a dynamic simulation whose outcomes will be interpreted by the cognitive agent; explanations and suggestions will be passed back to the user by the chatbot