81 research outputs found
Design of a solver for multi-agent epistemic planning
As the interest in Artificial Intelligence continues to grow it is becoming
more and more important to investigate formalization and tools that allow us to
exploit logic to reason about the world. In particular, given the increasing
number of multi-agents systems that could benefit from techniques of automated
reasoning, exploring new ways to define not only the world's status but also
the agents' information is constantly growing in importance. This type of
reasoning, i.e., about agents' perception of the world and also about agents'
knowledge of her and others' knowledge, is referred to as epistemic reasoning.
In our work we will try to formalize this concept, expressed through
epistemic logic, for dynamic domains. In particular we will attempt to define a
new action-based language for multi-agent epistemic planning and to implement
an epistemic planner based on it. This solver should provide a tool flexible
enough to be able to reason on different domains, e.g., economy, security,
justice and politics, where reasoning about others' beliefs could lead to
winning strategies or help in changing a group of agents' view of the world.Comment: In Proceedings ICLP 2019, arXiv:1909.07646. arXiv admin note: text
overlap with arXiv:1511.01960 by other author
Receding Horizon Re-ordering of Multi-Agent Execution Schedules
The trajectory planning for a fleet of Automated Guided Vehicles (AGVs) on a
roadmap is commonly referred to as the Multi-Agent Path Finding (MAPF) problem,
the solution to which dictates each AGV's spatial and temporal location until
it reaches it's goal without collision. When executing MAPF plans in dynamic
workspaces, AGVs can be frequently delayed, e.g., due to encounters with humans
or third-party vehicles. If the remainder of the AGVs keeps following their
individual plans, synchrony of the fleet is lost and some AGVs may pass through
roadmap intersections in a different order than originally planned. Although
this could reduce the cumulative route completion time of the AGVs, generally,
a change in the original ordering can cause conflicts such as deadlocks. In
practice, synchrony is therefore often enforced by using a MAPF execution
policy employing, e.g., an Action Dependency Graph (ADG) to maintain ordering.
To safely re-order without introducing deadlocks, we present the concept of the
Switchable Action Dependency Graph (SADG). Using the SADG, we formulate a
comparatively low-dimensional Mixed-Integer Linear Program (MILP) that
repeatedly re-orders AGVs in a recursively feasible manner, thus maintaining
deadlock-free guarantees, while dynamically minimizing the cumulative route
completion time of all AGVs. Various simulations validate the efficiency of our
approach when compared to the original ADG method as well as robust MAPF
solution approaches.Comment: IEEE Transactions on Robotics (T-Ro) preprint, 17 pages, 32 figure
A Comparison of SAT Encodings for Acyclicity of Directed Graphs
Many practical applications require synthesizing directed graphs that satisfy the acyclic constraint along with some side constraints. Several methods have been devised for encoding acyclicity of directed graphs into SAT, each of which is based on a cycle-detecting algorithm. The leaf-elimination encoding (LEE) repeatedly eliminates leaves from the graph, and judges the graph to be acyclic if the graph becomes empty at a certain time. The vertex-elimination encoding (VEE) exploits the property that the cyclicity of the resulting graph produced by the vertex-elimination operation entails the cyclicity of the original graph. While VEE is significantly smaller than the transitive-closure encoding for sparse graphs, it generates prohibitively large encodings for large dense graphs. This paper reports on a comparison study of four SAT encodings for acyclicity of directed graphs, namely, LEE using unary encoding for time variables (LEE-u), LEE using binary encoding for time variables (LEE-b), VEE, and a hybrid encoding which combines LEE-b and VEE. The results show that the hybrid encoding significantly outperforms the others
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