660,217 research outputs found
Domain independent goal recognition
Goal recognition is generally considered to follow plan recognition. The plan recognition problem is typically deďŹned to be that of identifying which plan in a given library of plans is being executed, given a sequence of observed actions. Once a plan has been identiďŹed, the goal of the plan can be assumed to follow. In this work, we address the problem of goal recognition directly, without assuming a plan library. Instead, we start with a domain description, just as is used for plan construction, and a sequence of action observations. The task, then, is to identify which possible goal state is the ultimate destination of the trajectory being observed. We present a formalisation of the problem and motivate its interest, before describing some simplifying assumptions we have made to arrive at a ďŹrst implementation of a goal recognition system, AUTOGRAPH. We discuss the techniques employed in AUTOGRAPH to arrive at a tractable approximation of the goal recognition problem and show results for the system we have implemented
Reconstructed Intentions in Collaborative Problem Solving Dialogues
We provide evidence that speech act recognition, is 1) difficult for humans to do and 2) likely to misidentify proposals involving reconstructed intentions. We examine the reliability of coding for speech acts in collaborative dialogues and we present an approach for recognizing reconstructed proposals using domain context and other more easily recognized features. 1 Introduction Speech act recognition plays a prominent role in dialogue understanding, in traditional approaches that infer a plan using plan construction operators [PA80], [LA90], [LC91, LC92], and in more recent techniques relying on statistical correlations or finite state machines [RM95, QDL + 97]. Both approaches recognize surface speech acts, using surface form and information provided by the discourse context and the discourse operators, or by a finite state approximation of the planning information. These approaches assume that it is (relatively) simple to recognize speech acts, and that speech acts are a requi..
Monitoring Teams by Overhearing: A Multi-Agent Plan-Recognition Approach
Recent years are seeing an increasing need for on-line monitoring of teams of
cooperating agents, e.g., for visualization, or performance tracking. However,
in monitoring deployed teams, we often cannot rely on the agents to always
communicate their state to the monitoring system. This paper presents a
non-intrusive approach to monitoring by 'overhearing', where the monitored
team's state is inferred (via plan-recognition) from team-members' routine
communications, exchanged as part of their coordinated task execution, and
observed (overheard) by the monitoring system. Key challenges in this approach
include the demanding run-time requirements of monitoring, the scarceness of
observations (increasing monitoring uncertainty), and the need to scale-up
monitoring to address potentially large teams. To address these, we present a
set of complementary novel techniques, exploiting knowledge of the social
structures and procedures in the monitored team: (i) an efficient probabilistic
plan-recognition algorithm, well-suited for processing communications as
observations; (ii) an approach to exploiting knowledge of the team's social
behavior to predict future observations during execution (reducing monitoring
uncertainty); and (iii) monitoring algorithms that trade expressivity for
scalability, representing only certain useful monitoring hypotheses, but
allowing for any number of agents and their different activities to be
represented in a single coherent entity. We present an empirical evaluation of
these techniques, in combination and apart, in monitoring a deployed team of
agents, running on machines physically distributed across the country, and
engaged in complex, dynamic task execution. We also compare the performance of
these techniques to human expert and novice monitors, and show that the
techniques presented are capable of monitoring at human-expert levels, despite
the difficulty of the task
Login Authentication with Facial Gesture Recognition
Facial recognition has proven to be very useful and versatile, from Facebook photo tagging and Snapchat filters to modeling fluid dynamics and designing for augmented reality. However, facial recognition has only been used for user login services in conjunction with expensive and restrictive hardware technologies, such as in smart phone devices like the iPhone x. This project aims to apply machine learning techniques to reliably distinguish user accounts with only common cameras to make facial recognition logins more accessible to website and software developers. To show the feasibility of this idea, we created a web API that recognizes a users face to log them in to their account, and we will create a simple website to test the reliability of our system. In this paper, we discuss our database-centric architecture model, use cases and activity diagrams, technologies we used for the website, API, and machine learning algorithms. We also provide the screenshots of our system, the user manual, and our future plan
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