A5050806660- AI-based Problem Solving and Planning
- Reinforcement Learning in Robotics
- Intelligent Tutoring Systems and Adaptive Learning
- Artificial Intelligence in Games
- Online Learning and Analytics
- Logic, Reasoning, and Knowledge
- Constraint Satisfaction and Optimization
- Topic Modeling
- Hate Speech and Cyberbullying Detection
- Digital Games and Media
- Gambling Behavior and Treatments
- Mobile Crowdsensing and Crowdsourcing
- Caching and Content Delivery
- Semantic Web and Ontologies
- Mobile Ad Hoc Networks
- Multi-Agent Systems and Negotiation
- Speech and Audio Processing
- Computational and Text Analysis Methods
- Speech Recognition and Synthesis
- Folklore, Mythology, and Literature Studies
- Natural Language Processing Techniques
- Social Media and Politics
- Language and cultural evolution
- Phonetics and Phonology Research
- Teaching and Learning Programming
Smart Information Flow Technologies (United States)
2022-2024
Drexel University
2015-2021
This paper focuses on tracing player knowledge in educational games. Specifically, given a set of concepts or skills required to master game, the goal is estimate likelihood with which current has mastery each those skills. The main contribution an approach that integrates machine learning and domain rules find when applied certain skill either succeeded failed. then as input standard module (such from Intelligent Tutoring Systems) perform tracing. We evaluate our context game called...
This article focuses on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tracing player knowledge</i> in educational games. Specifically, given a set of concepts or skills required to master game, the goal is estimate likelihood with which current has mastery each those skills. The main contribution work an approach that integrates machine learning and domain knowledge rules find when applied certain skill either succeeded failed. then as...
This paper defines a learning algorithm for plan grammars used recognition. The learns Combinatory Categorial Grammars (CCGs) that capture the structure of plans from set successful execution traces paired with goal actions. work is motivated by past on CCG algorithms natural language processing, and evaluated five well know planning domains.
This paper presents a new model of cooperative behavior based on the interaction plan recognition and automated planning. Based observations actions an "initiator" agent, "supporter" agent uses to hypothesize plans goals initiator. The supporter then proposes for set subgoals it will achieve help approach is demonstrated in open-source, virtual robot platform.
This paper focuses on the problem of scaling Combinatory Categorial Grammar (CCG)-based plan recognition to large CCG representations in context Real-Time Strategy (RTS) games. Specifically, we present a technique scale domain using Monte-Carlo Tree Search (MCTS). CCG-based planning and (like other domain-configurable frameworks) require definitions be either manually authored or learned from data. Prior work has demonstrated successful learning these data, but can very for complex...
Content dissemination in peer-to-peer mobile ad-hoc networks is subject to disruptions due erratic link performance and intermittent connectivity. Distributed protocols such as BitTorrent are now ubiquitously used for content wired Internet-scale networks, but not infrastructure-less, which makes them unsuitable MANETs. Our approach (called SISTO) a fully distributed torrent-based solution, with four key features: (i) freedom from any reliance on infrastructure; (ii) network topology aware...
Goal or intent recognition, where one agent recognizes the goals intentions of another, can be a powerful tool for effective teamwork and improving interaction between agents. Such reasoning challenging to perform, however, because observations an unreliable and, often, does not have access processes mental models other agent. Despite this difficulty, recent work has made great strides in addressing these challenges. In particular, two Artificial Intelligence (AI)-based approaches goal...
This paper presents a Combinatory Categorial Grammar-based game playing agent called μCCG for the Real-Time Strategy testbed μRTS. The key problem that tries to address is of adversarial planning in very large search space RTS games. In order this problem, we present new hierarchical algorithm based on Grammars (CCGs). grammar used by our planner automatically learned from sequences actions taken replay data. We provide an empirical analysis against agents CIG 2017 μRTS competition using...
Real-Time Strategy (RTS) games are an interesting environment to study challenging AI problems, such as real-time adversarial planning and opponent modeling. In this paper we focus on approaches that make use of replay data, which usually encode domain expert knowledge gameplay. Some these supervised learning learn player/agent strategy models thus rely replays being annotated with specific strategies or other labels. However, do not contain labels for strategies. The problem address in is...
This paper focuses on "tracing player knowledge" in educational games. Specifically, given a set of concepts or skills required to master game, the goal is estimate likelihood with which current has mastery each those skills. The main contribution an approach that integrates machine learning and domain knowledge rules find when applied certain skill either succeeded failed. then as input standard tracing module (such from Intelligent Tutoring Systems) perform tracing. We evaluate our context...
Word embedding models have been used in prior work to extract associations of intersectional identities within discourse concerning institutions power, but restricted its focus on narratives the nineteenth-century U.S. south. This paper leverages this and introduces an initial study association intersected with social media from Nigeria. Specifically, we use word trained tweets Nigeria (e.g., domestic, culture, etc.) provide insight into alignment institutions. Our experiments indicate that...