Sound and Relatively Complete Belief Hoare Logic for Statistical Hypothesis Testing Programs

We propose a new approach to formally describing the requirement for statistical inference and checking whether a program uses the statistical method appropriately. Specifically, we define belief Hoare logic (BHL) for formalizing and reasoning about the statistical beliefs acquired via hypothesis testing. This program logic is sound and relatively complete with respect to a Kripke model for hypothesis tests. We demonstrate by examples that BHL is useful for reasoning about practical issues in hypothesis testing. In our framework, we clarify the importance of prior beliefs in acquiring statistical beliefs through hypothesis testing, and discuss the whole picture of the justification of statistical inference inside and outside the program logic

Learning plan selection for BDI agent systems

Belief-Desire-Intention (BDI) is a popular agent-oriented programming approach for developing robust computer programs that operate in dynamic environments. These programs contain pre-programmed abstract procedures that capture domain know-how, and work by dynamically applying these procedures, or plans, to different situations that they encounter. Agent programs built using the BDI paradigm, however, do not traditionally do learning, which becomes important if a deployed agent is to be able to adapt to changing situations over time. Our vision is to allow programming of agent systems that are capable of adjusting to ongoing changes in the environment’s dynamics in a robust and effective manner. To this end, in this thesis we develop a framework that can be used by programmers to build adaptable BDI agents that can improve plan selection over time by learning from their experiences. These learning agents can dynamically adjust their choice of which plan to select in which situation, based on a growing understanding of what works and a sense of how reliable this understanding is. This reliability is given by a perceived measure of confidence, that tries to capture how well-informed the agent’s most recent decisions were and how well it knows the most recent situations that it encountered. An important focus of this work is to make this approach practical. Our framework allows learning to be integrated into BDI programs of reasonable complexity, including those that use recursion and failure recovery mechanisms. We show the usability of the framework in two complete programs: an implementation of the Towers of Hanoi game where recursive solutions must be learnt, and a modular battery system controller where the environment dynamics changes in ways that may require many learning and relearning phases

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

Golog speaks the BDI language

In this paper, we relate two of the most well developed approaches to agent-oriented programming, namely, BDI (Belief-Desire-Intention) style programming and 'Golog-like' high-level programming. In particular, we show how 'Golog-like' programming languages can be used to develop BDI-style agent systems. The contribution of this paper is twofold. First, it demonstrates how practical agent systems can be developed using high-level languages like Golog or IndiGolog. Second, it provides BDI languages a clear classical-logic-based semantics and a powerful logical foundation for incorporating new reasoning capabilities not present in typical BDI systems