Rational Agents: Prioritized Goals, Goal Dynamics, and Agent Programming Languages with Declarative Goals

I introduce a specification language for modeling an agent's prioritized goals and their dynamics. I use the situation calculus along with Reiter's solution to the frame problem and predicates for describing agents' knowledge as my base formalism. I further enhance this language by introducing a new sort of infinite paths. Within this language, I discuss how to systematically specify prioritized goals and how to precisely describe the effects of actions on these goals. These actions include adoption and dropping of goals and subgoals. In this framework, an agent's intentions are formally specified as the prioritized intersection of her goals. The ``prioritized'' qualifier above means that the specification must respect the priority ordering of goals when choosing between two incompatible goals. I ensure that the agent's intentions are always consistent with each other and with her knowledge. I investigate two variants with different commitment strategies. Agents specified using the ``optimizing'' agent framework always try to optimize their intentions, while those specified in the ``committed'' agent framework will stick to their intentions even if opportunities to commit to higher priority goals arise when these goals are incompatible with their current intentions. For these, I study properties of prioritized goals and goal change. I also give a definition of subgoals, and prove properties about the goal-subgoal relationship. As an application, I develop a model for a Simple Rational Agent Programming Language (SR-APL) with declarative goals. SR-APL is based on the ``committed agent'' variant of this rich theory, and combines elements from Belief-Desire-Intention (BDI) APLs and the situation calculus based ConGolog APL. Thus SR-APL supports prioritized goals and is grounded on a formal theory of goal change. It ensures that the agent's declarative goals and adopted plans are consistent with each other and with her knowledge. In doing this, I try to bridge the gap between agent theories and practical agent programming languages by providing a model and specification of an idealized BDI agent whose behavior is closer to what a rational agent does. I show that agents programmed in SR-APL satisfy some key rationality requirements

Agent Programming with Declarative Goals

A long and lasting problem in agent research has been to close the gap between agent logics and agent programming frameworks. The main reason for this problem of establishing a link between agent logics and agent programming frameworks is identified and explained by the fact that agent programming frameworks have not incorporated the concept of a `declarative goal'. Instead, such frameworks have focused mainly on plans or `goals-to-do' instead of the end goals to be realised which are also called `goals-to-be'. In this paper, a new programming language called GOAL is introduced which incorporates such declarative goals. The notion of a `commitment strategy' - one of the main theoretical insights due to agent logics, which explains the relation between beliefs and goals - is used to construct a computational semantics for GOAL. Finally, a proof theory for proving properties of GOAL agents is introduced. Thus, we offer a complete theory of agent programming in the sense that our theory provides both for a programming framework and a programming logic for such agents. An example program is proven correct by using this programming logic

infinite states verification in game-theoretic logics

Many practical problems where the environment is not in the system's control such as service orchestration and contingent and multi-agent planning can be modelled in game-theoretic logics. This thesis demonstrates that the verification techniques based on regression and fixpoint approximation introduced in De Giacomo, Lesperance and Pearce [DLP10] do work on several game-theoretic problems. De Giacomo, Lesperance and Pearce [DLP10] emphasize that their study is essentially theoretical and call for complementing their work with experimental studies to understand whether these techniques are effective in practical cases. Several example problems with varying properties have been developed and, although not exhaustive nor complete,, our results nevertheless demonstrate that the techniques work on some problems. Our results show that the methods introduced in [DLP10] work for infinite domains where very few verification methods are available and allow reasoning about a wide range of game problems. Our examples also demonstrate the use of a rich language for specifying temporal properties proposed in [DLP10]. While classical model checking is well known and utilized, it is mostly restricted to finite-state models. A important aspect of the work is the demonstration of the use and effectiveness of characteristic graphs (ClaBen and Lakemeyer [CL08]) in verifying properties of games in infinite domains. A special-purpose programming language GameGolog proposed in De Giacomo, Lesperance and Pearce [DLP10] allows such game-theoretic systems to be specified procedurally at a high-level of abstraction. We show its practicality to model game structures in a convenient way that combines declarative and procedural elements. We provided examples to show the verification of GameGolog specifications using characteristic graphs. This thesis also proposes a refinement to the formalism in [DLP10] to incorporate action constraints as a mechanism to incorporate user strategies and for the modeller to supply heuristic guidance in temporal property verification. It also presents an implementation of evaluation-based fixpoint verifier that handles Situation Calculus game structures, as well as GameGolog specifications, for temporal property verification in the initial or a given situation. The verifier supports player action constraints

Planning in BDI agent systems

 Belief-Desire-Intention (BDI) agent systems are a popular approach to developing agents for complex and dynamic environments. These agents rely on context sensitive expansion of plans, acting as they go, and consequently, they do not incorporate a generic mechanism to do any kind of “look-ahead” or offline planning. This is useful when, for instance, important resources may be consumed by executing steps that are not necessary for a goal; steps are not reversible and may lead to situations in which a goal cannot be solved; and side effects of steps are undesirable if they are not useful for a goal. In this thesis, we incorporate planning techniques into BDI systems. First, we provide a general mechanism for performing “look-ahead” planning, using Hierarchical Task Network (HTN) planning techniques, so that an agent may guide its selection of plans for the purpose of avoiding negative interactions between them. Unlike past work on adding such planning into BDI agents, which do so only at the implementation level without any precise semantics, we provide a solid theoretical basis for such planning. Second, we incorporate first principles planning into BDI systems, so that new plans may be created for achieving goals. Unlike past work, which focuses on creating low-level plans, losing much of the domain knowledge encoded in BDI agents, we introduce a novel technique where plans are created by respecting and reusing the procedural domain knowledge encoded in such agents; our abstract plans can be executed in the standard BDI engine using this knowledge. Furthermore, we recognise an intrinsic tension between striving for abstract plans and, at the same time, ensuring that unnecessary actions, unrelated to the specific goal to be achieved, are avoided. To explore this tension, we characterise the set of “ideal” abstract plans that are non-redundant while maximally abstract, and then develop a more limited but feasible account where an abstract plan is “specialised” into a plan that is non-redundant and as abstract as possible. We present theoretical properties of the planning frameworks, as well as insights into their practical utility

Towards Bridging the Gap between High-Level Reasoning and Execution on Robots

When reasoning about actions, e.g., by means of task planning or agent programming with Golog, the robot's actions are typically modeled on an abstract level, where complex actions such as picking up an object are treated as atomic primitives with deterministic effects and preconditions that only depend on the current state. However, when executing such an action on a robot it can no longer be seen as a primitive. Instead, action execution is a complex task involving multiple steps with additional temporal preconditions and timing constraints. Furthermore, the action may be noisy, e.g., producing erroneous sensing results and not always having the desired effects. While these aspects are typically ignored in reasoning tasks, they need to be dealt with during execution. In this thesis, we propose several approaches towards closing this gap.Comment: PhD Thesi

Approaches for action sequence representation in robotics: a review

Robust representation of actions and its sequences for complex robotic tasks would transform robot’s understand- ing to execute robotic tasks efficiently. The challenge is to under- stand action sequences for highly unstructured environments and to represent and construct action and action sequences. In this manuscript, we present a review of literature dealing with representation of action and action sequences for robot task planning and execution. The methodological review was conducted using Google Scholar and IEEE Xplore, searching the specific keywords. This manuscript gives an overview of current approaches for representing action sequences in robotics. We propose a classification of different methodologies used for action sequences representation and describe the most important aspects of the reviewed publications. This review allows the reader to understand several options that do exist in the research community, to represent and deploy such action representations in real robots