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• W2022138992 abstract "Extensive research in autonomous agents has studied actionselection, i,e. the problem of having an agent at any point in time choose actions that will best fulfill its goals. However, for many applications where a human interacts with the ngent, it is not enough for the agent to ‘do the right thing;’ it must also do it in the right way, i.e. so the user can understand what the agent is doing. Tom Porter terms this problem the ‘action-expression’ problem: what should the agent do at any point in order to best communicate its goals and activities to the user? Current agent architectures often have difficulty with action-expression because design of the agent is focused on internal problem-solving rather than external effect. Behaviorbased agents in particular tend to jump from behavior to behavior according to whatever best fulfills their internal needs, which can confuse a user trying to find a common thread in the agent’s activities. The system described here, the ,!hpreasivator, is based on a philosophy that what matters is not the internally-defined code as understood by the designer, but the impression the agent makes on the user. The Expressivator’s focus is on reducing the apparent randomness of agent behavior choice by adding transition beIIauior8, special behaviors that function to explain to the user the agent’s motivations in changing from one activity to another. In addition, the Expressivator offers a signmana~cmcnt suatem that keeps track of the visible signs the agent’s behavior has produced, allowing the agent to make decisions based not only on its internal idea of what it is doing, but also on the likely user perception of its behaviors. It bootstraps on the advantages of behavior-based systems, including reactivity, interruptability, and modularity, while allowing the agent-builder to design agents that explicitly communicate their goals and intentions to the user. 1 The Problem: Action-Expression Recently, an important research topic in agent architectures has been action-selection. The action-selection problem is commonly defined as determining, at any point in time, the Ikntkh IO ntakc digibdhrd topics ofilll or pnr~ ofhis nlnterinl for pcnonnl or clnssrooni use is gnnted willlout Ike provided U~nt the copies nre INN tllnde or distributed for profit or conanercial ndvanhge, the copyrih character animators have solved it on a daily basis for years. Many of John Lasseter’s well-known suggestions for computer graphics, for example, have to do with communicating a character’s actions and thinking effectively to the viewer [ll]. Action-expression has traditionally been difficult to solve in agent architectures, however, not because it is necessarily difhcult, but because it requires a different mindset about agents than the ones that have historically been dominant in AI. In AI, agents are traditionally thought of as problemsolvers, as ethological models, and/or as self-contained individuals. Action-expression, in contrast, demands that agents be thought, not in terms of their inherent properties, but in terms of their effect on an audience. This is in line with recent work in believable agents such as [20, 14, 24, 5, 11, which focus more and more on the audience’s perception of agents, rather than on an agent’s correctness per se. The most obvious approach to action-expression is to try to implement it directly using older architectures. However, experience with the Waggles and other believable systems in the Oz project has shown that it is not always possible to take this band-aid approach; layering an awareness of agent-as-representation on top of previously-built AI technology may simply be inadequate [14]. Rather, this change in viewpoint can deeply affect agent technology, which at times may need to be greatly m-thought." @default.
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• W2022138992 date "1998-01-01" @default.
• W2022138992 modified "2023-10-17" @default.
• W2022138992 title "Do the thing right" @default.
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• W2022138992 doi "https://doi.org/10.1145/280765.280770" @default.
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