Agents that learn to explain themselves
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Increasing believability in animated pedagogical agents
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Old tricks, new dogs: ethology and interactive creatures
Old tricks, new dogs: ethology and interactive creatures
Believable agents: building interactive personalities
Believable agents: building interactive personalities
Antiboxology: agent design in cultural context
Antiboxology: agent design in cultural context
Automated Assistants to Aid Humans in Understanding Team Behaviors
RoboCup-99: Robot Soccer World Cup III
Value-Driven Characters for Storytelling and Drama
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
An architecture for directing value-driven artificial characters
Agents for games and simulations II
Improvisation, emotion, video game
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
Evaluation of an affective model: COR-E
IVA'12 Proceedings of the 12th international conference on Intelligent Virtual Agents
A cognitive module in a decision-making architecture for agents in urban simulations
CAVE'12 Proceedings of the First international conference on Cognitive Agents for Virtual Environments
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For many applications, it is important that agents be not only correct, but also comprehensible to human users. Typically, people have tried to make agents' behavior and reasoning understandable by adding post-hoc special-purpose explanation systems, with often disappointing results. Here, I instead take the comprehensibility of agent behavior as a central agent design consideration from the start. I describe an agent architecture, the Expressivator, that supports comprehensibility on top of a behavior-based framework, using four technical innovations: (1) structuring the agent's behavior according to the signs and signifiers it is intended to communicate; (2) allowing the agent to keep track of its impression on the user with sign management, (3) using behavioral transitions to explain the reasons for agent, behavior, and (4) expressing behavioral interrelationships directly using meta-level controls.