Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven Robot Behavior

This article develops Probabilistic Hybrid Action Models (PHAMs), a realistic causal model for predicting the behavior generated by modern percept-driven robot plans. PHAMs represent aspects of robot behavior that cannot be represented by most action models used in AI planning: the temporal structure of continuous control processes, their non-deterministic effects, several modes of their interferences, and the achievement of triggering conditions in closed-loop robot plans. The main contributions of this article are: (1) PHAMs, a model of concurrent percept-driven behavior, its formalization, and proofs that the model generates probably, qualitatively accurate predictions; and (2) a resource-efficient inference method for PHAMs based on sampling projections from probabilistic action models and state descriptions. We show how PHAMs can be applied to planning the course of action of an autonomous robot office courier based on analytical and experimental results

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

Risk-minimizing program execution in robotic domains

Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 153-161).In this thesis, we argue that autonomous robots operating in hostile and uncertain environments can improve robustness by computing and reasoning explicitly about risk. Autonomous robots with a keen sensitivity to risk can be trusted with critical missions, such as exploring deep space and assisting on the battlefield. We introduce a novel, risk-minimizing approach to program execution that utilizes program flexibility and estimation of risk in order to make runtime decisions that minimize the probability of program failure. Our risk-minimizing executive, called Murphy, utilizes two forms of program flexibility, 1) flexible scheduling of activity timing, and 2) redundant choice between subprocedures, in order to minimize two forms of program risk, 1) exceptions arising from activity failures, and 2) exceptions arising from timing constraint violations in a program. Murphy takes two inputs, a program written in a nondeterministic variant of the Reactive Model-based Programming Language (RMPL) and a set of stochastic activity failure models, one for each activity in a program, and computes two outputs, a risk-minimizing decision policy and value function. The decision policy informs Murphy which decisions to make at runtime in order to minimize risk, while the value function quantifies risk. In order to execute with low latency, Murphy computes the decision policy and value function offline, as a compilation step prior to program execution. In this thesis, we develop three approaches to RMPL program execution. First, we develop an approach that is guaranteed to minimize risk. For this approach, we reason probabilistically about risk by framing program execution as a Markov Decision Process (MDP). Next, we develop an approach that avoids risk altogether. For this approach, we frame program execution as a novel form of constraint-based temporal reasoning. Finally, we develop an execution approach that trades optimality in risk avoidance for tractability. For this approach, we leverage prior work in hierarchical decomposition of MDPs in order to mitigate complexity. We benchmark the tractability of each approach on a set of representative RMPL programs, and we demonstrate the applicability of the approach on a humanoid robot simulator.by Robert T. Effinger, IV.Sc.D

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

Supporting adaptiveness of cyber-physical processes through action-based formalisms

Cyber Physical Processes (CPPs) refer to a new generation of business processes enacted in many application environments (e.g., emergency management, smart manufacturing, etc.), in which the presence of Internet-of-Things devices and embedded ICT systems (e.g., smartphones, sensors, actuators) strongly influences the coordination of the real-world entities (e.g., humans, robots, etc.) inhabitating such environments. A Process Management System (PMS) employed for executing CPPs is required to automatically adapt its running processes to anomalous situations and exogenous events by minimising any human intervention. In this paper, we tackle this issue by introducing an approach and an adaptive Cognitive PMS, called SmartPM, which combines process execution monitoring, unanticipated exception detection and automated resolution strategies leveraging on three well-established action-based formalisms developed for reasoning about actions in Artificial Intelligence (AI), including the situation calculus, IndiGolog and automated planning. Interestingly, the use of SmartPM does not require any expertise of the internal working of the AI tools involved in the system

State-of-the-art on evolution and reactivity

This report starts by, in Chapter 1, outlining aspects of querying and updating resources on the Web and on the Semantic Web, including the development of query and update languages to be carried out within the Rewerse project. From this outline, it becomes clear that several existing research areas and topics are of interest for this work in Rewerse. In the remainder of this report we further present state of the art surveys in a selection of such areas and topics. More precisely: in Chapter 2 we give an overview of logics for reasoning about state change and updates; Chapter 3 is devoted to briefly describing existing update languages for the Web, and also for updating logic programs; in Chapter 4 event-condition-action rules, both in the context of active database systems and in the context of semistructured data, are surveyed; in Chapter 5 we give an overview of some relevant rule-based agents frameworks

On the definition of non-player character behaviour for real-time simulated virtual environments.

Computer games with complex virtual worlds, which are populated by artificial characters and creatures, are the most visible application of artificial intelligence techniques. In recent years game development has been fuelled by dramatic advances in computer graphics hardware which have led to a rise in the quality of real-time computer graphics and increased realism in computer games. As a result of these developments video games are gaining acceptance and cultural significance as a form of art and popular culture. An important factor for the attainment of realism in games is the artificially intelligent behaviour displayed by the virtual entities that populate the games' virtual worlds. It is our firm belief that to further improve the behaviour of virtual entities, game AI development will have to mirror the advances achieved in game graphics. A major contributing factor for these advancements has been the advent of programmable shaders for real-time graphics, which in turn has been significantly simplified by the introduction of higher level programming languages for the creation of shaders. This has demonstrated that a good system can be vastly improved by the addition of a programming language. This thesis presents a similar (syntactic) approach to the definition of the behaviour of virtual entities in computer games. We introduce the term behaviour definition language (BDL), describing a programming language for the definition of game entity behaviour. We specify the requirements for this type of programming language, which are applied to the development and implementation of several behaviour definition languages, culminating in the design of a new game-genre independent behaviour definition (scripting) language. This extension programming language includes several game AI techniques within a single unified system, allowing the use of different methods of behaviour definition. A subset of the language (itself a BDL) was implemented as a proof of concept of this design, providing a framework for the syntactic definition of the behaviour of virtual entities in computer games

A belief-desire-intention architechture with a logic-based planner for agents in stochastic domains

This dissertation investigates high-level decision making for agents that are both goal and utility driven. We develop a partially observable Markov decision process (POMDP) planner which is an extension of an agent programming language called DTGolog, itself an extension of the Golog language. Golog is based on a logic for reasoning about action—the situation calculus. A POMDP planner on its own cannot cope well with dynamically changing environments and complicated goals. This is exactly a strength of the belief-desire-intention (BDI) model: BDI theory has been developed to design agents that can select goals intelligently, dynamically abandon and adopt new goals, and yet commit to intentions for achieving goals. The contribution of this research is twofold: (1) developing a relational POMDP planner for cognitive robotics, (2) specifying a preliminary BDI architecture that can deal with stochasticity in action and perception, by employing the planner.ComputingM. Sc. (Computer Science

Agent programming in the cognitive era

It is claimed that, in the nascent ‘Cognitive Era’, intelligent systems will be trained using machine learning techniques rather than programmed by software developers. A contrary point of view argues that machine learning has limitations, and, taken in isolation, cannot form the basis of autonomous systems capable of intelligent behaviour in complex environments. In this paper, we explore the contributions that agent-oriented programming can make to the development of future intelligent systems. We briefly review the state of the art in agent programming, focussing particularly on BDI-based agent programming languages, and discuss previous work on integrating AI techniques (including machine learning) in agent-oriented programming. We argue that the unique strengths of BDI agent languages provide an ideal framework for integrating the wide range of AI capabilities necessary for progress towards the next-generation of intelligent systems. We identify a range of possible approaches to integrating AI into a BDI agent architecture. Some of these approaches, e.g., ‘AI as a service’, exploit immediate synergies between rapidly maturing AI techniques and agent programming, while others, e.g., ‘AI embedded into agents’ raise more fundamental research questions, and we sketch a programme of research directed towards identifying the most appropriate ways of integrating AI capabilities into agent programs