This paper provides a general mechanism and a solid theoretical basis for performing planning within Belief-Desire-Intention (BDI) agents. BDI agent systems have emerged as one of the most widely used approaches to implementing intelligent behaviour in complex dynamic domains, in addition to which they have a strong theoretical background. However, these systems either do not include any built-in capacity for "lookahead" type of planning or they do it only at the implementation level without any precise defined semantics. In some situations, the ability to plan ahead is clearly desirable or even mandatory for ensuring success. Also, a precise definition of how planning can be integrated into a BDI system is highly desirable. By building on the underlying similarities between BDI systems and Hierarchical Task Network (HTN) planners, we present a formal semantics for a BDI agent programming language which cleanly incorporates HTN-style planning as a built-in feature. We argue that the resulting integrated agent programming language combines the advantages of both BDI agent systems and hierarchical offline planners.

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.

Deceptive path-planning involves finding a path such that the probability of an observer identifying its final destination-before it has been reachedis minimised. This paper formalises deception as it applies to path-planning and introduces the notion of a last deceptive point (LDP) which, when measured in terms of path completion, can be used to rank paths by their potential to deceive. Building on recent developments in probabilistic goalrecognition, we propose a formula to calculate an optimal LDP and present strategies for the generation of deceptive paths by both simulation ('showing the false') and dissimulation ('hiding the real').

IndiGolog is a programming language for autonomous agents that sense their environment and do planning as they operate. Instead of classical planning, it supports high-level program execution. The programmer provides a high-level nondeterministic program involving domain-specific actions and tests to perform the agent's tasks. The IndiGolog interpreter then reasons about the preconditions and effects of the actions in the program to find a legal terminating execution. To support this, the programmer provides a declarative specification of the domain (i.e., primitive actions, preconditions and effects, what is known about the initial state) in the situation calculus. The programmer can control the amount of nondeterminism in the program and how much of it is searched over. The language is rich and supports concurrent programming. Programs are executed online together with sensing the environment and monitoring for events, thus supporting the development of reactive agents. We discuss the language, its implementation, and applications that have been realized with it.

The behavior composition problem amounts to realizing a virtual desired module (e.g., a surveillance agent system) by suitably coordinating (and re-purposing) the execution of a set of available modules (e.g., a video camera, vacuum cleaner, a robot, etc.). In particular, we investigate techniques to synthesize a controller implementing a fully controllable target behavior by suitably coordinating available partially controllable behaviors that are to execute within a shared, fully observable, but partially predictable (i.e., non-deterministic), environment. Both behaviors and environment are represented as arbitrary finite state transition systems. The technique we propose is directly based on the idea that the controller job is to coordinate the concurrent execution of the available behaviors so as to "mimic" the target behavior. To this end, we exploit a variant of the formal notion of simulation to formally capture the notion of "mimicking", and we show that the technique proposed is sound and complete, optimal with respect to computational complexity, and robust for different kind of system failures. In addition, we demonstrate that the technique is well suited for highly efficient implementation based on synthesis by model checking technologies, by relating the problem to that of finding a winning strategy in a special safety game and explaining how to actually solve it using an existing verification tool. © 2013 Elsevier B.V. All rights reserved