Abstract Humans can make a plan by refining abstract actions. Dynamic logic, which enables reasoning about the dynamics of actions, does not support this form of planning. This paper investigates the extension of dynamic logic with a refinement relation that specifies how an abstract action can be refined into a more specific (composite) action. The paper investigates the properties of the refinement relation, the derivation of the refinement relation and a proof system based on a prefixed-tableau.

Inconsistent knowledge-bases can entail useful conclusions when using the three-valued semantics of the paraconsistent logic LP. However, the set of conclusions entailed by a consistent knowledge-base under the three-valued semantics is smaller than set of conclusions entailed by the knowledge-base under a two-valued semantics. Preferring conflict-minimal interpretations of the logic LP; i.e., LPm, reduces the gap between these two sets of conclusions.

The BDI architecture is one of the most popular architectures for agents with symbolic reasoning capabilities. To formally define the notions of Beliefs, Desires and Intentions, different formal logics have been proposed in the literature. Although these proposals often refer to the work of Bratman [2], none, however, correctly capture the form of practical reasoning that Bratman describes. What is lacking, is a proper characterization of the agent's behavior. The formal logics proposed so far, do not allow for an adequate characterization of the refinement of behaviors that Bratman describes.

In case of a plan failure, plan-repair is a more promising solution than replanning from scratch. The effectiveness of plan-repair depends on knowledge of which plan action failed and why. Therefore, in this paper, we propose an Extended Spectrum Based Diagnosis approach that efficiently pinpoints failed actions. Unlike Model Based Diagnosis (MBD), it does not require the fault models and behavioral descriptions of actions. Our approach first computes the likelihood of an action being faulty and subsequently proposes optimal probe locations to refine the diagnosis. We also exploit knowledge of plan steps that are instances of the same plan operator to optimize the selection of the most informative diagnostic probes. In this paper, we only focus on diagnostic aspect of plan-repair process.

Diagnosis of plan failures is an important subject in both single- and multi-agent planning. Plan diagnosis can be used to deal with plan failures in three ways: (i) to provide information necessary for the adjustment of the current plan or for the development of a new plan, (ii) to point out which equipment and/or agents should be repaired or adjusted to avoid further violation of the plan execution, and (iii) to identify the agents responsible for plan-execution failures. We introduce two general types of plan diagnosis: primary plan diagnosis identifying the incorrect or failed execution of actions, and secondary plan diagnosis that identifies the underlying causes of the faulty actions. Furthermore, three special cases of secondary plan diagnosis are distinguished, namely agent diagnosis , equipment diagnosis and environment diagnosis .

We consider a model-based diagnosis approach to the diagnosis of plans. Here, a plan performed by some agent(s) is considered as a system to be diagnosed. We introduce a simple formal model of plans and plan execution where it is assumed that the execution of a plan can be monitored by making partial observations of plan states. These observed states are used to compare them with states predicted based on (normal) plan execution. Deviations between observed and predicted states can be explained by qualifying some plan steps in the plan as behaving abnormally. A diagnosis is a subset of plan steps qualified as abnormal that can be used to restore the compatibility between the predicted and the observed partial state. Besides minimum and subset minimal diagnoses, we argue that in plan-based diagnosis maximum informative diagnoses should be considered as preferred diagnoses, too. The latter ones are diagnoses that make the strongest predictions with respect to partial states to be observed in the future. We show that in contrast to minimum diagnoses, finding a (subset minimal) maximum informative diagnosis can be achieved in polynomial time. Finally, we show how these diagnoses can be found efficiently if the plan is distributed over a number of agents.

In a multi-agent routing problem, agents must find the shortest-time path from source to destination while avoiding deadlocks and collisions with other agents. Agents travel over an infrastructure of resources (such as intersections and road segments between intersections), and each resource r has (i) a capacity C(r), which is the maximum number of agents that may occupy the resource at the same time, and (ii) a minimum travel time D(r). Example 1 illustrates this multi-agent routing problem.

In many domains successful execution of plans requires careful monitoring and repair. Diagnosis of plan execution supports this process by identifying causes of plan failure.

Argument systems are based on the idea that one can construct arguments for propositions—structured reasons justifying the belief in a proposition. Using defeasible rules, arguments need not be valid in all circumstances, therefore, it might be possible to construct an argument for a proposition as well as its negation. When arguments support conflicting propositions, one of the arguments must be defeated, which raises the question of which (sub‐) arguments can be subject to defeat. In legal argumentation, metarules determine the valid arguments by considering the last defeasible rule of each argument involved in a conflict. Since it is easier to evaluate arguments using their last rules, can a conflict be resolved by considering only the last defeasible rules of the arguments involved? We propose a new argument system where, instead of deriving a defeat relation between arguments, arguments for the defeat of defeasible rules are constructed. This system allows us to determine a set of valid (undefeated) arguments in linear time using an algorithm based on a JTMS, allows conflicts to be resolved using only the last rules of the arguments, allows us to establish a relation with Default Logic, and allows closure properties such as cumulativity to be proved. We propose an extension of the argument system based on a proposal for reasoning by cases in default logic.