A BDI agent programming language with failure handling, declarative goals, and planning

Abstract

Agents are an important technology that have the potential to take over contemporary methods for analysing, designing, and implementing complex software. The BeliefDesire-Intention (BDI) agent paradigm has proven to be one of the major approaches to intelligent agent systems, both in academia and in industry. Typical BDI agent-oriented programming languages rely on user-provided "plan libraries" to achieve goals, and online context sensitive subgoal selection and expansion. These allow for the development of systems that are extremely flexible and responsive to the environment, and as a result, well suited for complex applications with (soft) real-time reasoning and control requirements. Nonetheless, complex decision making that goes beyond, but is compatible with, run-time context-dependent plan selection is one of the most natural and important next steps within this technology. In this paper we develop a typical BDI-style agent-oriented programming language that enhances usual BDI programming style with three distinguished features: declarative goals, look-ahead planning, and failure handling. First, an account that mixes both procedural and declarative aspects of goals is necessary in order to reason about important properties of goals and to decouple plans from what these plans are meant to achieve. Second, lookahead deliberation about the effects of one choice of expansion over another is clearly desirable or even mandatory in many circumstances so as to guarantee goal achievability and to avoid undesired situations. Finally, a failure handling mechanism, suitably integrated with both declarative goals and planning, is required in order to model an adequate level of commitment to goals, as well as to be consistent with most real BDI implemented systems.

Keeping a Clear Separation between Goals and Plans

“…Rather than having goals simply triggered by rules, a planner can be used to select among available goals, as for example in CANPLAN [7]. The difference then from the reasoning model described above is that this time the reasoning allows the choice of goals to be prepared in advance starting form the current context.…”

“…The sole use of a goniometer allows for a stealth detection, i.e., detect without being detected, of boats which is important for some missions such as gas-freeing prevention 7 . If the boats were steady, the problem would be simple.…”

“…1). Note however that the successful completion of a plan does not necessarily guarantee the achievement of a goal, as goals can have success and failure conditions [7].…”

“…Several works [10,11,12,7] have argued for the interest of using declarative goals-to-be together with procedural goals-to-do, for decoupling goal achievement (the "to be" part) from plan execution (the "to do" part), giving the agents their pro-activeness, but also better flexibility and fault tolerance. Taking this delimitation a step further, we argue for the interest of separating a level where goal reasoning takes place -managing goal adoptions, dependencies, conflict resolution -from an action level where the agent interacts with its peers and environment.…”

Logical Foundations for a Rational BDI Agent Programming Language (Extended Version)

“…Recently, there has been much work on incorporating declarative goals in these APLs [7,28,21,5,27,22]. In addition to defining a set of plans that can be executed to try to achieve a goal, these programming languages also incorporate goals as declarative descriptions of the states of the world which are sought.…”