An operational semantics for a fragment of PRS

The Procedural Reasoning System (PRS) is arguably the first implementation of the Belief–Desire–Intention (BDI) approach to agent programming. PRS remains extremely influential, directly or indirectly inspiring the development of subsequent BDI agent programming languages. However, perhaps surprisingly given its centrality in the BDI paradigm, PRS lacks a formal operational semantics, making it difficult to determine its expressive power relative to other agent programming languages. This paper takes a first step towards closing this gap, by giving a formal semantics for a significant fragment of PRS. We prove key properties of the semantics relating to PRS-specific programming constructs, and show that even the fragment of PRS we consider is strictly more expressive than the plan constructs found in typical BDI languages

Agent programming in the cognitive era

It is claimed that, in the nascent ‘Cognitive Era’, intelligent systems will be trained using machine learning techniques rather than programmed by software developers. A contrary point of view argues that machine learning has limitations, and, taken in isolation, cannot form the basis of autonomous systems capable of intelligent behaviour in complex environments. In this paper, we explore the contributions that agent-oriented programming can make to the development of future intelligent systems. We briefly review the state of the art in agent programming, focussing particularly on BDI-based agent programming languages, and discuss previous work on integrating AI techniques (including machine learning) in agent-oriented programming. We argue that the unique strengths of BDI agent languages provide an ideal framework for integrating the wide range of AI capabilities necessary for progress towards the next-generation of intelligent systems. We identify a range of possible approaches to integrating AI into a BDI agent architecture. Some of these approaches, e.g., ‘AI as a service’, exploit immediate synergies between rapidly maturing AI techniques and agent programming, while others, e.g., ‘AI embedded into agents’ raise more fundamental research questions, and we sketch a programme of research directed towards identifying the most appropriate ways of integrating AI capabilities into agent programs

GROVE: A computationally grounded model for rational intention revision in BDI agents

A fundamental aspect of Belief-Desire-Intention (BDI) agents is intention revision. Agents revise their intentions in order to maintain consistency between their intentions and beliefs, and consistency between intentions. A rational agent must also account for the optimality of their intentions in the case of revision. To that end I present GROVE, a model of rational intention revision for BDI agents. The semantics of a GROVE agent is defined in terms of constraints and preferences on possible future executions of an agent’s plans. I show that GROVE is weakly rational in the sense of Grant et al. and imposes more constraints on executions than the operational semantics for goal lifecycles proposed by Harland et al. As it may not be computationally feasible to consider all possible future executions, I propose a bounded version of GROVE that samples the set of future executions, and state conditions under which bounded GROVE commits to a rational execution

GROVE: A computationally grounded model for rational intention revision in BDI agents

A fundamental aspect of Belief-Desire-Intention (BDI) agents is intention revision. Agents revise their intentions in order to maintain consistency between their intentions and beliefs, and consistency between intentions. A rational agent must also account for the optimality of their intentions in the case of revision. To that end I present GROVE, a model of rational intention revision for BDI agents. The semantics of a GROVE agent is defined in terms of constraints and preferences on possible future executions of an agent’s plans. I show that GROVE is weakly rational in the sense of Grant et al. and imposes more constraints on executions than the operational semantics for goal lifecycles proposed by Harland et al. As it may not be computationally feasible to consider all possible future executions, I propose a bounded version of GROVE that samples the set of future executions, and state conditions under which bounded GROVE commits to a rational execution

Goal Formation through Interaction in the Situation Calculus: A Formal Account Grounded in Behavioral Science

Goal reasoning has been attracting much attention in AI recently. Here, we consider how an agent changes its goals as a result of interaction with humans and peers. In particular, we draw upon a model developed in Behavioral Science, the Elementary Pragmatic Model (EPM). We show how the EPM principles can be incorporated into a sophisticated theory of goal change based on the Situation Calculus. The resulting logical theory supports agents with a wide variety of relational styles, including some that we may consider irrational or creative. This lays the foundations for building autonomous agents that interact with humans in a rich and realistic way, as required by advanced Human-AI collaboration applications

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

Agents are an important technology that have the potential to take over contemporary methods for analysing, designing, and implementing complex software. The Belief- Desire-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

handling, declarative goals, and planning

A BDI agent programming language with failur

Aborting, suspending, and resuming goals and plans in BDI agents

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

ActionPool : a novel dynamic task scheduling method for service robots

Service robots require the seamless utlisation of several technical disciplines. Most of the required technologies are sufficiently advanced to provide feasible solutions to be used in the designing of service robots. For instance, mechanical engineering, control theory, electronics and electrical engineering aspects of the design have all matured well. On the other hand, it is the perception and artificial intelligence that provide the means for modelling the environment and the knowledge which are lagging behind. The latter two disciples in their current state, greatly limit the complexity of the tasks which can be performed by service robots. In this thesis, an ActionPool method for representing task knowledge and executing multiple tasks simultaneously with service robots is presented. The method is based on a concept in which the actions that are ready for execution are placed into a pool and from those most suitable for the situation are selected one by one. The number of actions in a pool and the number of tasks are limited only by the available computational resources. The actions can belong to different tasks, and thus the action pool allows the robot's indivisible resource to be dynamically dealt out for various tasks requiring the resources. In the ActionPool method, the functional parts of the service robot are divided into resources and an action pool is assigned to each one of them. This way, numerous tasks can be executed simultaneously. The ActionPool method allows a natural way of dynamically adding and removing tasks to and from the robot's active execution. The action selection method can direct the perception processes to observe the relevant parts of the environment. The ActionPool method has been implemented on two different service robot platforms to verify the generic nature of the method. Several tasks have been executed successfully to validate the claims about the qualities of the method. Compared to previous approaches, this work provides a fresh execution- and contingency-centric vantage point to the well studied robot control problem