A dynamic epistemic framework for reasoning about conformant probabilistic plans

In this paper, we introduce a probabilistic dynamic epistemic logical framework that can be applied for reasoning and verifying conformant probabilistic plans in a single agent setting. In conformant probabilistic planning (CPP), we are looking for a linear plan such that the probability of achieving the goal after executing the plan is no less than a given threshold probability δ. Our logical framework can trace the change of the belief state of the agent during the execution of the plan and verify the conformant plans. Moreover, with this logic, we can enrich the CPP framework by formulating the goal as a formula in our language with action modalities and probabilistic beliefs. As for the main technical results, we provide a complete axiomatization of the logic and show the decidability of its validity problem

A Logic of Knowing How

In this paper, we propose a single-agent modal logic framework for reasoning about goal-direct "knowing how" based on ideas from linguistics, philosophy, modal logic and automated planning. We first define a modal language to express "I know how to guarantee phi given psi" with a semantics not based on standard epistemic models but labelled transition systems that represent the agent's knowledge of his own abilities. A sound and complete proof system is given to capture the valid reasoning patterns about "knowing how" where the most important axiom suggests its compositional nature.Comment: 14 pages, a 12-page version accepted by LORI

Knowing what to do:A logical approach to planning and knowing how

In dit proefschrift wordt vanuit logisch perspectief het maken van plannen en procedurele kennis (weten hoe) onderzocht. Conformant planning is het proberen te vinden van een plan om een doel te bereiken. Doelgerichte procedurele kennis betekent dat je weet wat je moet doen om een doel te bereiken. In dit proefschrift wordt een logisch raamwerk gepresenteerd waarin de veranderende kennis van een agent gevangen kan worden. Met dit logische raamwerk, kunnen de doelen opgevat worden als logische formules. In dit proefschrift worden ook doelgerichte procedurele kennis gemodelleert. Geïnspireerd door het idee van planning, worden in dit proefschrift verschillende soorten procedurele kennis onderscheiden, zoals in termen van conformant plans en in termen van strategieën. Met behulp van logische systemen voor deze noties, worden elementaire eigenschappen voor iedere soort procedurele kennis onderzocht. Het helpt ons ook om te zien welke eigenschappen de verschillende noties van procedurele kennis gemeen hebben en welke eigenschappen uniek zijn voor iedere notie van procedurele kennis

08361 Abstracts Collection -- Programming Multi-Agent Systems

From 31th August to 5th September, the Dagstuhl Seminar 08361 ``Programming Multi-Agent Systems\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

AMPLE: an anytime planning and execution framework for dynamic and uncertain problems in robotics

Acting in robotics is driven by reactive and deliberative reasonings which take place in the competition between execution and planning processes. Properly balancing reactivity and deliberation is still an open question for harmonious execution of deliberative plans in complex robotic applications. We propose a flexible algorithmic framework to allow continuous real-time planning of complex tasks in parallel of their executions. Our framework, named AMPLE, is oriented towards robotic modular architectures in the sense that it turns planning algorithms into services that must be generic, reactive, and valuable. Services are optimized actions that are delivered at precise time points following requests from other modules that include states and dates at which actions are needed. To this end, our framework is divided in two concurrent processes: a planning thread which receives planning requests and delegates action selection to embedded planning softwares in compliance with the queue of internal requests, and an execution thread which orchestrates these planning requests as well as action execution and state monitoring. We show how the behavior of the execution thread can be parametrized to achieve various strategies which can differ, for instance, depending on the distribution of internal planning requests over possible future execution states in anticipation of the uncertain evolution of the system, or over different underlying planners to take several levels into account. We demonstrate the flexibility and the relevance of our framework on various robotic benchmarks and real experiments that involve complex planning problems of different natures which could not be properly tackled by existing dedicated planning approaches which rely on the standard plan-then-execute loop

Conditional Partial Plans for Rational Situated Agents Capable of Deductive Reasoning and Inductive Learning

Rational, autonomous agents that are able to achieve their goals in dynamic, partially observable environments are the ultimate dream of Artificial Intelligence research since its beginning. The goal of this PhD thesis is to propose, develop and evaluate a framework well suited for creating intelligent agents that would be able to learn from experience, thus becoming more efficient at solving their tasks. We aim to create an agent able to function in adverse environments that it only partially understands. We are convinced that symbolic knowledge representations are the best way to achieve such versatility. In order to balance deliberation and acting, our agent needs to be emph{time-aware}, i.e. it needs to have the means to estimate its own reasoning and acting time. One of the crucial challenges is to ensure smooth interactions between the agent's internal reasoning mechanism and the learning system used to improve its behaviour. In order to address it, our agent will create several different conditional partial plans and reason about the potential usefulness of each one. Moreover it will generalise whatever experience it gathers and use it when solving subsequent, similar, problem instances. In this thesis we present on the conceptual level an architecture for rational agents, as well as implementation-based experimental results confirming that a successful lifelong learning of an autonomous artificial agent can be achieved using it

Foundations of Human-Aware Planning -- A Tale of Three Models

abstract: A critical challenge in the design of AI systems that operate with humans in the loop is to be able to model the intentions and capabilities of the humans, as well as their beliefs and expectations of the AI system itself. This allows the AI system to be "human- aware" -- i.e. the human task model enables it to envisage desired roles of the human in joint action, while the human mental model allows it to anticipate how its own actions are perceived from the point of view of the human. In my research, I explore how these concepts of human-awareness manifest themselves in the scope of planning or sequential decision making with humans in the loop. To this end, I will show (1) how the AI agent can leverage the human task model to generate symbiotic behavior; and (2) how the introduction of the human mental model in the deliberative process of the AI agent allows it to generate explanations for a plan or resort to explicable plans when explanations are not desired. The latter is in addition to traditional notions of human-aware planning which typically use the human task model alone and thus enables a new suite of capabilities of a human-aware AI agent. Finally, I will explore how the AI agent can leverage emerging mixed-reality interfaces to realize effective channels of communication with the human in the loop.Dissertation/ThesisDoctoral Dissertation Computer Science 201