There will be three sets of lectures by the following distinguished lecturers.
Natural Language Generation, Traditional Approaches and Research Directions
Human communication involves language understanding and production. The understanding part receives most of the attention in Natural Language Processing (NLP), but the subfield of Natural Language Generation (NLG) has been researching solutions to the challenges of language production for more than 50 years. A common misconception is that NLG is simply the inverse of understanding. However, the problems are related but different, as NLG concerns with the issue of choice: given certain information to communicate, it needs to choose among a wide variety of forms given communicative intentions and an understanding of the shared information with the hearer. In these lectures, we will start discussing traditional approaches, deeply tied to research systems and linguistic theories. We will then dive into new deep learning approaches that are rejuvenating the field.
Dr. Duboue has 20 years of experience on AI / NLP research and development. After obtaining a doctorate degree at Columbia University in NY, he moved to IBM Research to pursue work on Question Answering, where he was part of the team that beat the Jeopardy! champions in 2011. Since then he has focused on consulting for startups, accelerating the access of technology to its potential users through his Vancouver, Canada company Textualization Software Ltd.
Reasoning About Actions: From Automata to LTLf/LDLf Synthesis and Planning
In this course we will study AI planning for goals expressed over finite traces, instead of states. We will look at goals specified in two specific logics (i) LTLf, i.e., LTL interpreted over finite traces, which has the expressive power of FOL and star-free regular expressions over finite stings; and (ii) LDLf, i.e., Linear-time Dynamic Logic on finite traces, which has the expressive power of MSO and full regular expressions. We will review the main results and algorithmic techniques to handle planning in deterministic domains, and especially planning in nondeterministic domains, both under full and partial observability. We will also briefly consider stochastic domains. Moreover, we will draw connections with verification and reactive synthesis. The main catch is that working with these logics can be based on manipulation of regular automata on finite strings, plus standard forms of planning.
- Lecture 1: Automata (NFAs/DFAs) seen as trace recognizers to formally describe and verify models — (90min)
- Lecture 2: LTLf/LDLf specification and goals (and connection with NFAs/DFAs) — (90min)
- Lecture 3: Planning and Synthesis in nondeterministic domains (through games over DFAs) — (90min)
Giuseppe De Giacomo is full professor in Computer Science and Engineering at Universita di Roma “La Sapienza". His research activity has concerned theoretical, methodological and realization aspects in different areas of AI and CS, most prominently Knowledge Representation and Reasoning, Reasoning about Actions, Generalized Planning, Autonomous Agents, Service Composition and Orchestration, Process Modeling, Data Management and Integration. He is AAAI Fellow, ACM Fellow, and EurAI Fellow.