© 2007 Tom Beckman Features:  Are autonomous software entities that act as a user’s assistant to perform discrete tasks, simplifying or completely automating these tasks  Perform tasks based on knowledge of the user, task domain, and environment  Constantly monitor their environment to trigger actions  Perceive their environment through sensors, receive stimuli that trigger action, operate under constraints, and then perform actions on their environment  Use knowledge about the interests and priorities of people to perform routine tasks such as automatically screening, directing, revising, and responding to information Basic structure: Consists of goal, environment, sensors, and actuators Agent performance/behavior depends on:  The goal or performance measure that defines the criterion of success  The agent’s prior knowledge of the environment  The actions that the agent can perform  The agent’s perception sequence to date  The agent’s ability to learn what it can to compensate for partial or incorrect prior knowledge Agent Basics

© 2007 Tom Beckman After FSU Center for Performance Technology Communication:  Understand user goals, preferences, skills, interests, and constraints  Communicate knowledge to user about the task being performed  Alert user as to progress and status of task Autonomy:  Has a purpose and task  Acts to achieve task until it is fulfilled  Work and launch actions independent of user or other actors  Appropriate level of autonomy – doesn’t overstep Adaptive:  Learn from experience about its tasks and about user preferences  Adapt its behavior based upon a combination of user feedback (both passive—usage and active) and environmental factors (stimuli patterns) Dynamic:  Detect changes in the environment and react in a timely manner  Operate as a continuously running process without starting and stopping for each process or task Agents – Ideal Characteristics

© 2007 Tom Beckman After FSU Center for Performance Technology Advisory Agents – Assist in complex help or diagnostic systems Filtering Agents:  Remove data that does not match the user’s profile  Helps to reduce information overload Navigation Agents – Remember shortcuts, site bookmarks, and pre-load caching information Monitoring Agents:  Alert user to certain events  Provide information when data are created, retrieved, updated, or deleted Recommender Agents – Take information from previous user behaviors and make recommendations based on these behaviors Workflow Management Agents Retrieval Agents – Are more sophisticated search engines such as semantic search Interface Agents – Add presentation, speech, and natural language capabilities System Agents – Assist with the management of the computing environment Types of Agents

© 2007 Tom Beckman Collects content across all media types (TV, radio, newspaper, magazines, blogs, business conferences, and podcasts) Agents can prioritize content based on user’s profile User Profile Attributes:  Topics  People  Organizations  Prior usage and alert history Rules are developed about content that are most authoritative:  Authors  Publishers  Types Agents continually remind user of great content Features of Intelligent Alerts (After Paul Allen)

© 2007 Tom Beckman Greater intelligence in agents:  More AI – machine learning (NN, GA, FL) and expert systems (RBS & CBR)  Autonomous and proactive learning  Provide context, but not just content  Just-in-time, and just the right amount of information provided Agents easier to develop and interact with:  User developed, improved, and maintained  Interact via speech and gesture recognition Improved anthropomorphic features:  Understanding user speech, gestures, animation, facial expression, and non-verbal communication  Expressing gestures, animation, facial expression, and non-verbal communication Agent collaboration:  Agents talking to other agents  Agents interacting in a coordinated manner to perform larger tasks  Use of the Semantic Web to provide context Future of Agents (Steve Knode)

Advantages of Agents (after Peter Knode) © 2007 Tom Beckman Accuracy:  Adaptive ability ensures that intended result is produced despite changes in the environment  Provide context-sensitive help  Step-by-step instruction leads and guides users through complex tasks and reduces the potential for error Convenience:  More effective use of user’s time  Situates user in a computational work environment  Provides relevant information and executes actions as soon as possible  Remembers user history and preferences Personification:  More timely and consistent application of LL  Enhanced knowledge capture  More permanent problem resolution (as opposed to “point solutions”)  More “stickiness” in enforcing behavioral change