Deliberation over goals is a fundamental feature of intelligent agent systems. In this article we provide pragmatic but principled mechanisms for quantifying the level of completeness of goals in a Belief-Desire-Intention (BDI) agent. Our approach leverages previous work on resource and effects summarization which we extend by accommodating both dynamic resource summaries and goal effects, while also allowing a non-binary quantification of goal completeness. We treat both goals of accomplishment (achievement goals) and goals of monitoring (maintenance goals). We reconcile such practical computation of progress estimates of goals of both types with an earlier theoretical perspective on rnBDI goal completeness, and thus extend the theoretical framework to include maintenance goals. Our computational mechanisms have been implemented in the abstract agent language CAN. We also provide a case study in an autonomous rover domain.@en

User-supplied domain control knowledge in the form of hierarchically structured Goal-Plan Trees (GPTs) is at the heart of a number of approaches to reasoning about action. Reasoning with GPTs connects the AAMAS community with other communities such as automated planning, and forms the foundation for important reasoning capabilities, especially intention progression in Belief-Desire-Intention (BDI) agents. Research on GPTs has a long history but suffers from fragmentation and lack of common terminology, data formats, and enabling tools. One way to address this fragmentation is through a competition. Competitions are increasingly being used as a means to foster research and challenge the state of the art. For example, the AAMAS conference has a number of associated competitions, such as the Trading Agent Competition, while agent research is showcased at competitions such as RoboCup. We therefore issue a call for a Goal-Plan Tree Contest, with the ambition of drawing together a community and incentivizing research in intention progression.@en

Intelligent agents designed to work in complex, dynamic environments such as e-commerce must respond robustly and flexibly to environmental and circumstantial changes, including the actions of other agents. An agent must have the capability to deliberate about appropriate courses of action, which may include reprioritising tasks—whether goals or associated plans—aborting or suspending tasks, or scheduling tasks in a particular order. In this article we study mechanisms to enable principled suspend, resuming, and aborting of goals and plans within a Belief-Desire-Intention (BDI) agent architecture. We give a formal and combined operational semantics for these actions in an abstract agent language (CAN), thus providing a general mechanism that can be incorporated into several BDI-based agent platforms. The abilities enabled by our semantics provides an agent designer greater flexibility to direct agent operation, offering a generic means to manage the status of goals. We demonstrate the reasoning abilities enabled on a document workflow scenario.@en

We extend our earlier work on quantifying the level of completeness of achievement goals in BDI agents [8], to encompass maintenance goals. We both characterize what it means for a maintenance goal to be partially complete in terms of its relevancy, and sketch an efficient computational mechanism for an agent to compute dynamic estimates of the progress of its maintenance goals. We also discuss the relationship between our computation of progress estimate with an earlier theoretical perspective on BDI goal completeness.

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A fundamental feature of intelligent agents is their ability to deliberate over their goals. Operating in an environment that may change in unpredictable ways, an agent needs to regularly evaluate whether its current set of goals is the most appropriate set to pursue. The management of goals is thus a key aspect of an agent's architecture. Focusing on BDI agents, we consider the various types of goals studied in the literature, including both achievement and maintenance goals. We develop a detailed description of goal states (such as whether goals have been suspended or not), and a comprehensive suite of operations that may be applied to goals (including dropping, aborting, suspending and resuming them). We provide an operational semantics corresponding to this detailed description in an abstract agent language (CAN), and demonstrate on a detailed real-life scenario. The three key contributions of our generic framework for goal states and transitions are (1) to encompass both goals of accomplishment and rich goals of monitoring, (2) to provide the first specification of abort and suspend for all the common goal types, and (3) to account for plan execution as well as the dynamics of subgoaling. Our semantics clarifies how an agent can manage its goals, based on the decisions that it chooses to make, and further provides a foundation for correctness verification of agent behaviour.

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Given the current set of intentions an autonomous agent may have, intention selection is the agent's decision which intention it should focus on next. Often, in the presence of conflicts, the agent has to choose between multiple intentions. One factor that may play a role in this deliberation is the level of completeness of the intentions. To that end, this paper provides pragmatic but principled mechanisms for quantifying the level of completeness of goals in a BDI-style agent. Our approach leverages previous work on resource and effects summarization but we go beyond by accommodating both dynamic resource summaries and goal effects, while also allowing a non-binary quantification of goal completeness. We demonstrate the computational approach on an autonomous robot case study.@en

Often, such as in the presence of conflicts, an agent must choose between multiple intentions. The level of completeness of the intentions can be a factor in this deliberation. We sketch a pragmatic but principled mechanism for quantifying the level of completeness of goals in a Belief-Desire-Intention-like agent. Our approach leverages previous work on resource and effects summarization but we go beyond by accommodating both dynamic resource summaries and goal effects, while also allowing a non-binary quantification of goal completeness.

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