Conference proceeding
Scaling up CCG-Based Plan Recognition via Monte-Carlo Tree Search
2019 IEEE Conference on Games (CoG), v 2019-, pp 1-8
Aug 2019
Abstract
This paper focuses on the problem of scaling Combinatory Categorial Grammar (CCG)-based plan recognition to large CCG representations in the context of Real-Time Strategy (RTS) games. Specifically, we present a technique to scale plan recognition to large domain representations using Monte-Carlo Tree Search (MCTS). CCG-based planning and plan recognition (like other domain-configurable planning frameworks) require domain definitions to be either manually authored or learned from data. Prior work has demonstrated successful learning of these CCG domain definitions from data, but these representations can be very large for complex application domains. We propose a MCTS-based approach to search for explanations and predict the goal of a given sequence of observed actions. We evaluate our approach on the RTS game AI testbed microRTS. Our experimental results show our method scales better to these large, learned CCGs than previous CCG-based approaches.
Metrics
3 Record Views
10 citations in Scopus
Details
- Title
- Scaling up CCG-Based Plan Recognition via Monte-Carlo Tree Search
- Creators
- Pavan Kantharaju - Drexel UniversitySantiago Ontanon - Drexel UniversityChristopher W Geib - Drexel University
- Publication Details
- 2019 IEEE Conference on Games (CoG), v 2019-, pp 1-8
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Scopus ID
- 2-s2.0-85073105529
- Other Identifier
- 991019173420504721