A5041982650- AI-based Problem Solving and Planning
- Logic, Reasoning, and Knowledge
- Semantic Web and Ontologies
- Multi-Agent Systems and Negotiation
- Formal Methods in Verification
- Service-Oriented Architecture and Web Services
- Reinforcement Learning in Robotics
- Business Process Modeling and Analysis
- Constraint Satisfaction and Optimization
- Advanced Software Engineering Methodologies
- Model-Driven Software Engineering Techniques
- Bayesian Modeling and Causal Inference
- Logic, programming, and type systems
- Topic Modeling
- Machine Learning and Algorithms
- Advanced Database Systems and Queries
- Software Engineering Research
- Ethics and Social Impacts of AI
- Fault Detection and Control Systems
- Artificial Intelligence in Games
- Natural Language Processing Techniques
- Data Stream Mining Techniques
- Complex Systems and Decision Making
- Receptor Mechanisms and Signaling
- Advanced Data Processing Techniques
University of Toronto
2015-2024
Vector Institute
2019-2024
Schwartz/Reisman Emergency Medicine Institute
2021-2024
Institute for Technology & Society
2021-2022
Samsung (United States)
2021
Stanford University
1999-2005
University of New Brunswick
2004
Knowledge Systems Institute
2002
Laboratoire d'Informatique de Paris-Nord
2002
Palo Alto Research Center
1997
The authors propose the markup of Web services in DAML family Semantic languages. This enables a wide variety agent technologies for automated service discovery, execution, composition and interoperation. present one such technology composition.
Web services -- Web-accessible programs and devices - are a key application area for the Semantic Web. With proliferation of evolution towards comes opportunity to automate various tasks. Our objective is enable markup automated reasoning technology describe, simulate, compose, test, verify compositions services. We take as our starting point DAML-S DAML+OIL ontology describing capabilities define semantics relevant subset in terms first-order logical language. hand, we encode service...
Current industry standards for describing Web Services focus on ensuring interoperability across diverse platforms, but do not provide a good foundation automating the use of Services. Representational techniques being developed Semantic can be used to augment these standards. The resulting Service specifications enable development software programs that interpret descriptions unfamiliar and then employ those services satisfy user goals. OWL-S ("OWL Services") is set notations expressing...
Reinforcement learning (RL) methods usually treat reward functions as black boxes. As such, these must extensively interact with the environment in order to discover rewards and optimal policies. In most RL applications, however, users have program function and, hence, there is opportunity make visible – show function’s code agent so it can exploit internal structure learn policies a more sample efficient manner. this paper, we how accomplish idea two steps. First, propose machines, type of...
Preparation requires technical research and development, as well adaptive, proactive governance
Web services --Web-accessible programs and devices -are a key application area for the Semantic Web.With proliferation of evolution towards comes opportunity to automate various tasks.Our objective is enable markup automated reasoning technology describe, simulate, compose, test, verify compositions services.We take as our starting point DAML-S DAML+OIL ontology describing capabilities define semantics relevant subset in terms first-order logical language.With hand, we encode service...
A key element to realizing the Semantic Web is developing a suitably rich language for encoding and describing content. Such must have well defined semantics, be sufficiently expressive describe complex interrelationships constraints between objects, amenable automated manipulation reasoning with acceptable limits on time resource requirements. component of services vision creation services. DAML-S such it DAML+OIL ontology that coalition researchers created support from DARPA.
In Reinforcement Learning (RL), an agent is guided by the rewards it receives from reward function. Unfortunately, may take many interactions with environment to learn sparse rewards, and can be challenging specify functions that reflect complex reward-worthy behavior. We propose using machines (RMs), which are automata-based representations expose function structure, as a normal form representation for functions. show how specifications of in various formal languages, including LTL other...
We address the problem of computing a policy for fully observable non-deterministic (FOND) planning problems. By focusing on relevant aspects state world, we introduce series improvements to previous art and extend applicability our planner, PRP, work in an online setting. The use relevance allows be exponentially more succinct representing solution FOND some domains. Through introduction new techniques avoiding deadends determining sufficient validity conditions, PRP has potential compute...
We examine the problem of learning models that characterize high-level behavior a system based on observation traces. Our aim is to develop are human interpretable. To this end, we introduce Linear Temporal Logic (LTL) formula parsimoniously captures given set positive and negative example approach LTL exploits symbolic state representation, searching through space labeled skeleton formulae construct an alternating automaton observed behavior, from which can be read off. Construction...
Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well other agents. However, planning involving nested beliefs is known be computationally challenging. In this work, we address task synthesizing plans that necessitate reasoning We plan from perspective a single agent with potential for goals and actions non-homogeneous co-present observations, ability one if it were another. formally characterize our...
In this paper we examine the general problem of generating preferred explanations for observed behavior with respect to a model dynamical system. This arises in diversity applications including diagnosis systems and activity recognition. We provide logical characterization notion an explanation. To generate identify exploit correspondence between explanation generation planning. The determination good requires additional domain-specific knowledge which represent as preferences over...
Temporally extended goals are critical to the specification of a diversity real-world planning problems. Here we examine problem non-deterministic with temporally specified in linear temporal logic (LTL), interpreted over either finite or infinite traces. Unlike existing LTL planners, place no restrictions on our formulae beyond those necessary distinguish from interpretations. We generate plans by compiling into instances described Planning Domain Definition Language that solved...