Conference proceedings talk, ACM & CS-CAN INFO Annual Canadian Celebration of Women in Computing Conference (CAN-CWIC), Toronto, ON, Canada
Linear Temporal Logic (LTL) can be combined with Automated Planning and LLMs in order to successfully create logical, believable, and coherent stories about time.
Conference proceedings talk, ACM & CS-CAN INFO Annual Canadian Celebration of Women in Computing Conference (CAN-CWIC), Toronto, ON, Canada
Issues of bias and unreliabilty in LLMS, and how they can be mitigated using a verification step such as a valid plan.
Guest Lecture, University of Maryland Baltimore County, Virtual
A survey of techniques that combine Automated Planning with Natural Language text generation to create narratives (stories) that are logical, coherent,and believable. Guest lecture for course CMSC 491/691 - Interactive Fiction and Text Generation at UMBC.
Tutorial, The International Conference on Automated Planning and Scheduling (ICAPS) 2024, Banff, AB, Canada
Conference proceedings talk, AAAI/SIGAI Doctoral Consortium, Association for the Advancement of Artificial Intelligence (AAAI), Vancouver, BC, Canada
Automated Planning can therefore be combined with Natural Language text generation to create narratives (stories) that are logical, coherent, and believable. A planning model provides scaffolding to an LLM so that the LLM’s language generation is context-dependent, in order to allow users to create more coherent, logical, and believable stories in a variety of domains.
Conference proceedings talk, The International Conference on Automated Planning and Scheduling (ICAPS) SPARK workshop, Virtual
We demonstrate the use of a planning model that provides scaffolding to an LLM so that its language generation is context dependent in order to create more coherent and believable stories in a variety of domains.
Conference proceedings talk, The International Conference on Automated Planning and Scheduling (ICAPS) KEPS workshop, Virtual
The goal of this preliminary work is to predict the next completion in PDDL code, based on previous and surrounding text. Generating valid PDDL code is a key component in creating robust planners. Thus, the ability to generate PDDL code will be extremely useful to PDDL practitioners for the purpose of solving planning problems. It further opens the door to providing a source of inspiration for the modeller. The main contribution of our approach is a language model built using Recurrent Neural Networks (RNNs) that is trained on existing PDDL domains, which can be used to generate PDDL-like code.
Conference proceedings talk, Association for Computing Machinery (ACM) Symposium DocEng 2018, Halifax, NS
Extracting key terms from technical documents allows us to write effective documentation that is specific and clear, with minimum ambiguity and confusion caused by nearly synonymous but different terms. For instance, in order to avoid confusion, the same object should not be referred to by two different names (e.g. “hydraulic oil filter”). In the modern world of commerce, clear terminology is the hallmark of successful RFPs (Requests for Proposal) and is therefore a key to the growth of competitive organizations. While Automatic Term Extraction (ATE) is a well-developed area of study, its applications in the technical domain have been sparse and constrained to certain narrow areas such as the biomedical research domain. We present a method for Automatic Term Extraction (ATE) for the technical domain based on the use of part-of-speech features and common words information.