A Strategic/Tactical Architecture for Planning in Dynamic Environments (original) (raw)

Merging strategic and tactical planning in dynamic, uncertain environments

Proceedings Eighth Conference on Artificial Intelligence for Applications

This paper presents a new approach to planning in dynamic and uncertain environments. Planning is viewed as a process in which an agent's long term goals are transformed into short term tasks and objectives, given the agent's strategy and the context of planning. The developed model allows for a dynamic balance between long term strategic planning and short term tactical planning.The notion of a strategy hierarchy is introduced to explicitly represent the process of strategy formulation and refinement. A combination of rules and plan scripts is used. Rules are used for representing domain knowledge and reasoning about strategic choices. Plan scripts are used for representing specific tasks and objectives. The uncertainty calculi of RUM/PRIMO [2], [6] are used for supporting reasoning under uncertainty. In the proposed model, it is also possible to achieve a seamless integration of case-based reasoning into the planning process. These ideas have been implemented in a system called MARS, which plans in the financial domain of mergers and acquisitions. I. PLANNING IN DYNAMIC AND UNCERTAIN DOMAINS N this section, we motivate the need for dynamic planning, I describe relevant prior research, and outline the structure of the paper.

Tactical planning using heuristics

Proceedings of the 14th Belgium-Netherlands Artificial Intelligence Conference (BNAIC'02)}, 2014

Modern transportation problems are highly dynamic and time critical. A planning system for transportation problems must therefore include efficient and flexible planning and replanning strategies. In this paper we introduce a general agent-based framework for highly dynamic order-based transportation planning problems where a tactical planner and several operational planners (one for each transport agent) are distinguished. In particular we discuss the role of the tactical planner responsible for dynamic task allocation of orders ...

A Framework for Modelling Tactical Decision-Making in Autonomous Systems

Journal of Systems and Software, 2015

There is an increasing need for autonomous systems that exhibit effective decision-making in unpredictable environments. However, the design of autonomous decision-making systems presents considerable challenges, particularly when they have to achieve their goals within a dynamic context. Tactics designed to handle unexpected environmental change, or attack by an adversary, must balance the need for reactivity with that of remaining focused on the system's overall goal. The lack of a design methodology and supporting tools for representing tactics makes them difficult to understand, maintain and reuse. This is a significant problem in the design of tactical decision-making systems. We describe a methodology and accompanying tool, TDF (Tactics Development Framework), based on the BDI (Beliefs, Desires, Intentions) paradigm. TDF supports structural modelling of missions, goals, scenarios, input/output, messaging and procedures, and generates skeleton code that reflects the overall design. TDF has been evaluated through comparison with UML, indicating that it provides significant benefits to those building autonomous, tactical decision-making systems.

Planning in the Tactical Air Domain

1995

TAcAm-SoAR is a reactive system that uses recognition-driven problem solving to plan and generate behavior in the domain of tactical air combat simulation. Our current research efforts focus on integrating more deliberative planning and learning mechanisms into the system. This paper discusses characteristics of the domain that influence potential planning solutions, together with our approach for integrating reactive and deliberative

Decision-Theoretic Planning with Multiple Execution Architectures*

1993

Two tools to facilitate decision making in complex, real-time domains are discussed. Multiple execution architectures are four implementations of the agent function, a function that receives percepts from the environment as input and outputs an action choice. The four execution architectures are defined by the different knowledge types that each uses. Depending on the domain and agent capabilities, each execution architecture has different speed and correctness properties. Metalevel control of planning computes the value of information of planning to compare to the utility of executing the current plan. Examples are presented from an autonomous, underwater vehicle domain.

Implementing planning as tactical reasoning

1992

I n this p a p e r we present a system (called MRG) for the development of planners that have t o work in real-world, complex and unpredictable application domains. The idea underlying MRG is that domain independent problem solving architectures, instead of featuring powerful but fixed control mechanisms, should provide powerful and flexible tools for the definition of domain dependent control mechanisms. Complex strategies and control mechanisms are uniformly represented an MRG b y tactics, explicit data structures that can be reused, modified, reasoned about and executed. A t the m oment, MRG is being successfully used within a c o mplex real world application under development at IRS T.

Strategic positioning in tactical scenario planning

Proceedings of the 10th annual conference on Genetic and evolutionary computation - GECCO '08, 2008

Capability planning problems are pervasive throughout many areas of human interest with prominent examples found in defense and security. Planning provides a unique context for optimization that has not been explored in great detail and involves a number of interesting challenges which are distinct from traditional optimization research.

Planning under uncertainty in dynamic domains

1997

Planning, the process of nding a course of action which can be executed to achieve some goal, is an important and well-studied area of AI. One of the central assumptions of classical AI-based planning is that after performing an action the resulting state can be predicted completely and with certainty. This assumption has allowed the development of planning algorithms that provably achieve their goals, but it has also hindered the use of planners in many real-world applications because of their inherent uncertainty. Recently, several planners have been implemented that reason probabilistically about the outcomes of actions and the initial state of a planning problem. However, their representations and algorithms do not scale well enough to handle large problems with many sources of uncertainty. This thesis introduces a probabilistic planning algorithm that can handle such problems by focussing on a smaller set of relevant sources of uncertainty, maintained as the plan is developed. This is achieved by using the candidate plan to constrain the sources of uncertainty that are considered, incrementally considering more sources as they are shown to be relevant. The algorithm is demonstrated in an implemented planner, called Weaver, that can handle uncertainty about actions taken by external agents, in addition to the kinds of uncertainty handled in previous planners. External agents may cause many simultaneous changes to the world that are not relevant to the success of a plan, making the ability t o determine and ignore irrelevant e v ents a crucial requirement for an e cient planner. Three additional techniques are presented that improve the planner's e ciency in a n umber of domains. First, the possible external events are analyzed before planning time to produce factored Markov c hains which can greatly speed up the probabilistic evaluation of the plan when structural conditions are met. Second, domainindependent heuristics are introduced for choosing an incremental modi cation to apply to the current plan. These heuristics are based on the observation that the failure of the candidate plan can be used to condition the probability that the modi cation will be successful. Third, analogical replay is used to share planning e ort across branches of the conditional plan. Empirical evidence shows that Weaver can create high-probability plans in a planning domain for managing the clean-up of oil spills at sea.