Goal-based Planning Research Articles

On-board Automated Mission Planning for Spacecraft Autonomy: A Survey

Traditionally, space mission operations are based on preplanned activities fulfilled by the ground segment. Due to restricted visibility windows and communication delays, some space systems cannot be completely controlled from the ground in real-time. A way to overcome this issue is to equip the satellite flight software with an autonomous decision-making system such as onboard automated planning techniques. This paper presents a comprehensive survey on goal-based mission planning to increase the level of spacecraft autonomy. The most used onboard planning techniques for space missions such as temporal and hierarchical planning are presented in details. We describe several onboard planners and compare them by design choices and performance results. Research trends in goal-based planning for the space domain are also outlined. This paper highlights the automated mission planning, whose state of the art has not been deeply explored by other surveys on autonomous spacecraft.

Goal-based composition of scalable hybrid analytics for heterogeneous architectures

Crafting scalable analytics in order to extract actionable business intelligence is a challenging endeavour, requiring multiple layers of expertise and experience. Often, this expertise is irreconcilably split between an organisation’s engineers and subject matter domain experts. Previous approaches to this problem have relied on technically adept users with tool-specific training.Such an approach has a number of challenges: Expertise — There are few data-analytic subject domain experts with in-depth technical knowledge of compute architectures; Performance — Analysts do not generally make full use of the performance and scalability capabilities of the underlying architectures; Heterogeneity — calculating the most performant and scalable mix of real-time (on-line) and batch (off-line) analytics in a problem domain is difficult; Tools — Supporting frameworks will often direct several tasks, including, composition, planning, code generation, validation, performance tuning and analysis, but do not typically provide end-to-end solutions embedding all of these activities.In this paper, we present a novel semi-automated approach to the composition, planning, code generation and performance tuning of scalable hybrid analytics, using a semantically rich type system which requires little programming expertise from the user. This approach is the first of its kind to permit domain experts with little or no technical expertise to assemble complex and scalable analytics, for hybrid on- and off-line analytic environments, with no additional requirement for low-level engineering support.This paper describes (i) an abstract model of analytic assembly and execution, (ii) goal-based planning and (iii) code generation for hybrid on- and off-line analytics. An implementation, through a system which we call Mendeleev, is used to (iv) demonstrate the applicability of this technique through a series of case studies, where a single interface is used to create analytics that can be run simultaneously over on- and off-line environments. Finally, we (v) analyse the performance of the planner, and (vi) show that the performance of Mendeleev’s generated code is comparable with that of hand-written analytics.

Approximate Policy Iteration with a Policy Language Bias: Solving Relational Markov Decision Processes

We study an approach to policy selection for large relational Markov Decision Processes (MDPs). We consider a variant of approximate policy iteration (API) that replaces the usual value-function learning step with a learning step in policy space. This is advantageous in domains where good policies are easier to represent and learn than the corresponding value functions, which is often the case for the relational MDPs we are interested in. In order to apply API to such problems, we introduce a relational policy language and corresponding learner. In addition, we introduce a new bootstrapping routine for goal-based planning domains, based on random walks. Such bootstrapping is necessary for many large relational MDPs, where reward is extremely sparse, as API is ineffective in such domains when initialized with an uninformed policy. Our experiments show that the resulting system is able to find good policies for a number of classical planning domains and their stochastic variants by solving them as extremely large relational MDPs. The experiments also point to some limitations of our approach, suggesting future work.