The Robot Operating System (ROS) is a widely used framework for building robotic systems. It offers a wide variety of reusable packages and a pattern for new developments. It is up to developers how to combine these elements and integrate them with decision-making for autonomous behavior. The feature of such decision-making that is in general valued the most is safety assurance. In this research preview, we present a formal approach for generating safe autonomous decision-making in ROS. We first describe how to improve our existing static verification approach to verify multi-goal multi-agent decision-making. After that, we describe how to transition from the improved static verification approach to the proposed runtime verification approach. An initial implementation of this research proposal yields promising results.

Long-term autonomy requires autonomous systems to adapt as their capabilities no longer perform as expected. To achieve this, a system must first be capable of detecting such changes. Creating and maintaining a system ontology is a comprehensive solution for this; an agent-maintained formal selfmodel will take the role of this system ontology. It would act as a repository of information about all the processes and functionality of the autonomous system, forming a systematic approach for detecting action failures. Our work will focus on Belief-Desire-Intention (BDI) [25] programming languages as they are well known for their use in developing intelligent agents [1, 6, 16, 21].

This paper presents aplib, a Java library for programming intelligent agents, featuring BDI and multi agency, but adding on top of it a novel layer of tactical programming inspired by the domain of theorem proving. Aplib is also implemented in such a way to provide the fluency of a Domain Specific Language (DSL). Compared to dedicated BDI agent programming languages such as JASON, 2APL, or GOAL,aplib's embedded DSL approach does mean that \aplib\ programmers will still be limited by Java syntax, but on other hand they get all the advantages that Java programmers get: rich language features (object orientation, static type checking, $\lambda$-expression, libraries, etc), a whole array of development tools, integration with other technologies, large community, etc.

In many logic-based BDI agent programming languages, plan selection involves inferencing over some underlying knowledge representation. While context-sensitive plan selection facilitates the development of flexible, declarative programs, the overhead of evaluating repeated queries to the agent's beliefs and goals can result in poor run time performance. In this paper we present an approach to multi-cycle query caching for logic-based BDI agent programming languages. We extend the abstract performance model presented in (Alechina et al. 2012) to quantify the costs and benefits of caching query results over multiple deliberation cycles. We also present results of experiments with prototype implementations of both single- and multi-cycle caching in three logic-based BDI agent platforms, which demonstrate that significant performance improvements are achievable in practice.

PODTGolog [Rens, 2010] is a Golog dialect attempting Broadly speaking, my research concerns combining to deal with partially observable MDP (POMDP) logic of action and POMDP theory in a coherent, environments. PODTGolog has not been given a mathematical theoretically sound language for agent programming.

In this work we will present an integration of a query-answering argumentation approach with an abstract agent programming language. Agents will argumentatively reason via queries, using information of their mental components. Special context-based queries will be used to model the interaction between mental components. Deliberation and execution semantics of the proposed integration are presented.