SCATEAgent: Context-Aware Software Agents for Multi-Modal Travel

Yin, Min; Griss, Martin

doi:10.1007/3-7643-7363-6_5

Cited by 9 publications

(6 citation statements)

References 20 publications

(5 reference statements)

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“…The HP Laboratories Cool Agent project [4] [5], continued later as the UCSC ScateAgent project [6], developed a series of context-aware personal and team assistant agents to manage information, arrange meetings, help software engineering teams and offer travel advice. Each of these used rule-based systems in conjunction with personal preferences and context, access to calendar information as well as mobile devices.…”

Section: Cool Agent Personal Assistantmentioning

confidence: 99%

Mobile Context-Aware Personal Messaging Assistant

Buthpitiya¹,

Madamanchi²,

Kommaraju³

et al. 2010

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. A previous study shows that busy professionals receive in excess of 50 emails per day of which approximately 23% require immediate attention, 13% require attention later and 64% are unimportant and typically ignored. The flood of emails impact mobile users even more heavily. Flooded inboxes cause busy professionals to spend considerable amounts of time searching for important messages, and there has been much research into automating the process using email content for classification; but we find email priority depends also on user context. In this paper we describe the Personal Messaging Assistant (PMA), an advanced rule-based email management system which considers user context and email content. Context information is gathered from various sources including mobile phones, indoor and outdoor locationing systems, and calendars. PMA uses separate scales of importance and urgency to prioritize emails and to decide on an appropriate action, such as SMS to user, defer to later, file or forward. Initial results yield 96% recall and 88% precision in importance classification of emails; 95% recall and 92% precision in urgency classification of emails. PMA shows a 30X reduction in false-negative rates over existing systems. A key contribution of our work has been to leverage an extensible set of context information, gathered in a mobile environment, for the classification of emails and customizable decision making.

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Section: Cool Agent Personal Assistantmentioning

confidence: 99%

Mobile Context-Aware Personal Messaging Assistant

Buthpitiya¹,

Madamanchi²,

Kommaraju³

et al. 2010

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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“…Using agents to communicate via PDAs and a wireless network, the ATIS provides the user with a personalized route advice taking into account his preferences. In addition, Yin and Griss [42] present an agentbased multi-modal traveler information system that fuses information from multiple sources on routes, congestion, incidents, weather, alternative transit modes and schedules, resulting in a user-friendly personalized assistant to a single traveler. However, both [33] and [42] present ATISs for single users.…”

Section: B Related Work and Paper Positioningmentioning

confidence: 99%

“…In addition, Yin and Griss [42] present an agentbased multi-modal traveler information system that fuses information from multiple sources on routes, congestion, incidents, weather, alternative transit modes and schedules, resulting in a user-friendly personalized assistant to a single traveler. However, both [33] and [42] present ATISs for single users. Further, in [35] a multi-agent system is presented addressing a set of requests in a temporal window by a vehicle routing with a time window approach.…”

Section: B Related Work and Paper Positioningmentioning

confidence: 99%

A Multi-Agent Advanced Traveler Information System for Optimal Trip Planning in a Co-Modal Framework

Dotoli

Zgaya

Russo³

et al. 2017

IEEE Trans. Intell. Transport. Syst.

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We present an advanced traveler information system (ATIS) for public and private transportation, including vehicle sharing and pooling services. The ATIS uses an agentbased architecture and multi-objective optimization to answer trip planning requests from multiple users in a co-modal setting, considering vehicle preferences and conflicting criteria. At each set of users' requests, the transportation network is represented by a co-modal graph that allows decomposing the trip planning problem into smaller tasks: the shortest routes between the network nodes are determined and then combined to obtain possible itineraries. Using multi-objective optimization, the set of uservehicle-route combinations according to the users' preferences is determined, ranking all possible route agents' coalitions. The ATIS is tested for the real case study of the Lille metropolitan area (Nord Pas de Calais, France).

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“…Products such as the Onstar system introduced in 1995 that works with an automotive network, GPS and a communication network to aid drivers, show the beginning of the convergence of heterogeneous sensor-driven services for human needs [58]. Demonstration systems that use the convergence of data from heterogeneous sources [59] [60,61], demonstrated that a system can recognize and encourage a person to reduce driving errors by simply watching the speed, steering wheel, gas, brake, cup holder and blinkers.…”

Section: Context-aware Computingmentioning

confidence: 99%