6 research outputs found
Strategy Synthesis for Autonomous Agents Using PRISM
We present probabilistic models for autonomous agent search and retrieve missions derived from Simulink models for an Unmanned Aerial Vehicle (UAV) and show how probabilistic model checking and the probabilistic model checker PRISM can be used for optimal controller generation. We introduce a sequence of scenarios relevant to UAVs and other autonomous agents such as underwater and ground vehicles. For each scenario we demonstrate how it can be modelled using the PRISM language, give model checking statistics and present the synthesised optimal controllers. We conclude with a discussion of the limitations when using probabilistic model checking and PRISM in this context and what steps can be taken to overcome them. In addition, we consider how the controllers can be returned to the UAV and adapted for use on larger search areas
Convex Hull Monte-Carlo Tree Search
This work investigates Monte-Carlo planning for agents in stochastic
environments, with multiple objectives. We propose the Convex Hull Monte-Carlo
Tree-Search (CHMCTS) framework, which builds upon Trial Based Heuristic Tree
Search and Convex Hull Value Iteration (CHVI), as a solution to multi-objective
planning in large environments. Moreover, we consider how to pose the problem
of approximating multiobjective planning solutions as a contextual multi-armed
bandits problem, giving a principled motivation for how to select actions from
the view of contextual regret. This leads us to the use of Contextual Zooming
for action selection, yielding Zooming CHMCTS. We evaluate our algorithm using
the Generalised Deep Sea Treasure environment, demonstrating that Zooming
CHMCTS can achieve a sublinear contextual regret and scales better than CHVI on
a given computational budget.Comment: Camera-ready version of paper accepted to ICAPS 2020, along with
relevant appendice
Multi-objective policy generation for mobile robots under probabilistic time-bounded guarantees
We present a methodology for the generation of mobile robot
controllers which offer probabilistic time-bounded guarantees
on successful task completion, whilst also trying to satisfy
soft goals. The approach is based on a stochastic model
of the robot’s environment and action execution times, a set
of soft goals, and a formal task specification in co-safe linear
temporal logic, which are analysed using multi-objective
model checking techniques for Markov decision processes.
For efficiency, we propose a novel two-step approach. First,
we explore policies on the Pareto front for minimising expected
task execution time whilst optimising the achievement
of soft goals. Then, we use this to prune a model with more
detailed timing information, yielding a time-dependent policy
for which more fine-grained probabilistic guarantees can
be provided. We illustrate and evaluate the generation of policies
on a delivery task in a care home scenario, where the
robot also tries to engage in entertainment activities with the
patients