Archive for July, 2008

On-Line Case-Based Plan Adaptation for Real-Time Strategy Games

Traditional artificial intelligence techniques do not perform well in applications such as real-time strategy games because of the extensive search spaces which need to be explored. In addition, this exploration must be carried out on-line during performance time; it cannot be precomputed. We have developed on-line case-based planning techniques that are effective in such domains. In this paper, we extend our earlier work using ideas from traditional planning to inform the real-time adaptation of plans. In our framework, when a plan is retrieved, a plan dependency graph is inferred to capture the relations between actions in the plan. The plan is then adapted in real-time using its plan dependency graph. This allows the system to create and adapt plans in an efficient and effective manner while performing the task. The approach is evaluated using WARGUS, a well-known real-time strategy game.

Read the paper:

On-Line Case-Based Plan Adaptation for Real-Time Strategy Games

by Neha Sugandh, Santi Ontañón, Ashwin Ram

23rd AAAI Conference on Artificial Intelligence (AAAI-08), Chicago, IL, July 2008

New Directions in Goal-Driven Learning

Goal-Driven Learning (GDL) views learning as a strategic process in which the learner attempts to identify and satisfy its learning needs in the context of its tasks and goals. This is modeled as a planful process where the learner analyzes its reasoning traces to identify learning goals, and composes a set of learning strategies (modeled as planning operators) into a plan to learn by satisfying those learning goals.

Traditional GDL frameworks were based on traditional planners. However, modern AI systems often deal with real-time scenarios where learning and performance happen in a reactive real-time fashion, or are composed of multiple agents that use different learning and reasoning paradigms. In this talk, I discuss new GDL frameworks that handle such problems, incorporating reactive and multi-agent planning techniques in order to manage learning in these kinds of AI systems.

About this talk:

New Directions in Goal-Driven Learning

by Ashwin Ram

Invited keynote at International Conference on Machine Learning (ICML-08) Workshop on Planning to Learn, Helsinki, Finland, July 2008

Subjectivity Analysis for Questions in QA Communities

In this paper we investigate how to automatically determine the subjectivity orientation of questions posted by real users in community question answering (CQA) portals. Subjective questions seek answers containing private states, such as personal opinion and experience. In contrast, objective questions request objective, verifiable information, often with support from reliable sources. Knowing the question orientation would be helpful not only for evaluating answers provided by users, but also for guiding the CQA engine to process questions more intelligently. Our experiments on Yahoo! Answers data show that our method exhibits promising performance.

Read the paper:

Subjectivity Analysis for Questions in QA Communities

by Baoli Li, Yandong Liu, Ashwin Ram, Ernie Garcia, Eugene Agichtein

31st Annual International ACM SIGIR Conference (ACM-SIGIR-08), Singapore, July 2008