This work presents Drake, a dynamic executive for temporal plans with choice. Dynamic plan execution strategies allow an autonomous agent to react quickly to unfolding events, improving the robustness of the agent. Prior work developed methods for dynamically dispatching Simple Temporal Networks, and further research enriched the expressiveness of the plans executives could handle, including discrete choices, which are the focus of this work. However, in some approaches to date, these additional choices induce significant storage or latency requirements to make flexible execution possible. Drake is designed to leverage the low latency made possible by a preprocessing step called compilation, while avoiding high memory costs through a compact representation. We leverage the concepts of labels and environments, taken from prior work in Assumption-based Truth Maintenance Systems (ATMS), to concisely record the implications of the discrete choices, exploiting the structure of the plan to avoid redundant reasoning or storage. Our labeling and maintenance scheme, called the Labeled Value Set Maintenance System, is distinguished by its focus on properties fundamental to temporal problems, and, more generally, weighted graph algorithms. In particular, the maintenance system focuses on maintaining a minimal representation of non-dominated constraints. We benchmark Drake's performance on random structured problems, and find that Drake reduces the size of the compiled representation by a factor of over 500 for large problems, while incurring only a modest increase in run-time latency, compared to prior work in compiled executives for temporal plans with discrete choices.

This article is devoted to the study of methods to change defeasible logic programs (de.l.p.s) which are the knowledge bases used by the Defeasible Logic Programming (DeLP) interpreter. DeLP is an argumentation formalism that allows to reason over potentially inconsistent de.l.p.s. Argument Theory Change (ATC) studies certain aspects of belief revision in order to make them suitable for abstract argumentation systems. In this article, abstract arguments are rendered concrete by using the particular rule-based defeasible logic adopted by DeLP. The objective of our proposal is to define prioritized argument revision operators \`a la ATC for de.l.p.s, in such a way that the newly inserted argument ends up undefeated after the revision, thus warranting its conclusion. In order to ensure this warrant, the de.l.p. has to be changed in concordance with a minimal change principle. To this end, we discuss different minimal change criteria that could be adopted. Finally, an algorithm is presented, implementing the argument revision operations.

The ViewGen model of belief ascription assumes that each agent involved in a conversation has a belief space which includes models of what other parties to the conversation believe. The distinctive notion is that a basic procedure, called belief ascription, allows belief spaces to be amalgamated so as to model the updating and augmentation of belief environments. In this paper we extend the ViewGen model to a more general account of reference phenomena, in particular by the notion of a reachable ascription set (RAS) that links intensional objects across belief environments so as to locate the most heuristically plausible referent at a given point in a conversation. The key notion is the location and attachment of entities that may be under different descriptions, the consequent updating of the system's beliefs about other agents by default, and the role in that process of a speaker's and hearer's protocols that ensure that the choice is the appropriate one. An important characteristic of this model is that each communicator considers nothing beyond his own belief space. A conclusion we shall draw is that traditional binary distinctions in this area (like de dicto/de re and attributive/referential) neither classify the examples effectively nor do they assist in locating referents, whereas the single procedure we suggest does both. We also suggest ways in which this analysis can also illuminate other traditional distinctions such as referential and attributive use. The description here is not on an implemented system with results but a theoretical tool to be implemented within an established dialogue platform (such as Wilks et al. 2011).

The ViewGen model of belief ascription assumes that each agent involved in a conversation has a belief space which includes models of what other parties to the conversation believe. The distinctive notion is that a basic procedure, called belief ascription, allows belief spaces to be amalgamated so as to model the updating and augmentation of belief environments. In this paper we extend the ViewGen model to a more general account of reference phenomena, in particular by the notion of a reachable ascription set (RAS) that links intensional objects across belief environments so as to locate the most heuristically plausible referent at a given point in a conversation. The key notion is the location and attachment of entities that may be under different descriptions, the consequent updating of the system's beliefs about other agents by default, and the role in that process of a speaker's and hearer's protocols that ensure that the choice is the appropriate one. An important characteristic of this model is that each communicator considers nothing beyond his own belief space. A conclusion we shall draw is that traditional binary distinctions in this area (like de dicto/de re and attributive/referential) neither classify the examples effectively nor do they assist in locating referents, whereas the single procedure we suggest does both. We also suggest ways in which this analysis can also illuminate other traditional distinctions such as referential and attributive use. The description here is not on an implemented system with results but a theoretical tool to be implemented within an established dialogue platform (such as Wilks et al. 2011).

Reasoning with commonsense science knowledge is an important challenge for Artificial Intelligence. This paper presents a system that revises its knowledge in a commonsense science domain by constructing and evaluating explanations. Domain knowledge is represented using qualitative model fragments, which are used to explain phenomena via model formulation. Metareasoning is used to (1) score competing explanations numerically along several dimensions and (2) evaluate preferred explanations for global consistency. Inconsistencies cause the system to favor alternative explanations and thereby change its beliefs. We simulate the belief changes of several students during clinical interviews about how the seasons change. We show that qualitative models accurately represent student knowledge and that our system produces and revises a sequence of explanations similar those of the students.

Model checking is a promising technology, which has been applied for verification of many hardware and software systems. In this paper, we introduce the concept of model update towards the development of an automatic system modification tool that extends model checking functions. We define primitive update operations on the models of Computation Tree Logic (CTL) and formalize the principle of minimal change for CTL model update. These primitive update operations, together with the underlying minimal change principle, serve as the foundation for CTL model update. Essential semantic and computational characterizations are provided for our CTL model update approach. We then describe a formal algorithm that implements this approach. We also illustrate two case studies of CTL model updates for the well-known microwave oven example and the Andrew File System 1, from which we further propose a method to optimize the update results in complex system modifications.

Goal recognition is the task of inferring users’ goals from sequences of observed actions. By enabling player-adaptive digital games to dynamically adjust their behavior in concert with players’ changing goals, goal recognition can inform adaptive decision making for a broad range of entertainment, training, and education applications. This paper presents a goal recognition framework based on Markov logic networks (MLN). The model’s parameters are directly learned from a corpus of actions that was collected through player interactions with a non-linear educational game. An empirical evaluation demonstrates that the MLN goal recognition framework accurately predicts players’ goals in a game environment with multiple solution paths.

We study the performance of different message passing algorithms in the two dimensional Edwards Anderson model. We show that the standard Belief Propagation (BP) algorithm converges only at high temperature to a paramagnetic solution. Then, we test a Generalized Belief Propagation (GBP) algorithm, derived from a Cluster Variational Method (CVM) at the plaquette level. We compare its performance with BP and with other algorithms derived under the same approximation: Double Loop (DL) and a two-ways message passing algorithm (HAK). The plaquette-CVM approximation improves BP in at least three ways: the quality of the paramagnetic solution at high temperatures, a better estimate (lower) for the critical temperature, and the fact that the GBP message passing algorithm converges also to non paramagnetic solutions. The lack of convergence of the standard GBP message passing algorithm at low temperatures seems to be related to the implementation details and not to the appearance of long range order. In fact, we prove that a gauge invariance of the constrained CVM free energy can be exploited to derive a new message passing algorithm which converges at even lower temperatures. In all its region of convergence this new algorithm is faster than HAK and DL by some orders of magnitude.

Standard belief change assumes an underlying logic containing full classical propositional logic. However, there are good reasons for considering belief change in less expressive logics as well. In this paper we build on recent investigations by Delgrande on contraction for Horn logic. We show that the standard basic form of contraction, partial meet, is too strong in the Horn case. This result stands in contrast to Delgrandes conjecture that orderly maxichoice is the appropriate form of contraction for Horn logic. We then define a more appropriate notion of basic contraction for the Horn case, influenced by the convexity property holding for full propositional logic and which we refer to as infra contraction. The main contribution of this work is a result which shows that the construction method for Horn contraction for belief sets based on our infra remainder sets corresponds exactly to Hanssons classical kernel contraction for belief sets, when restricted to Horn logic. This result is obtained via a detour through contraction for belief bases. We prove that kernel contraction for belief bases produces precisely the same results as the belief base version of infra contraction. The use of belief bases to obtain this result provides evidence for the conjecture that Horn belief change is best viewed as a 'hybrid' version of belief set change and belief base change. One of the consequences of the link with base contraction is the provision of a representation result for Horn contraction for belief sets in which a version of the Core-retainment postulate features.

This paper extends the framework of partially observable Markov decision processes (POMDPs) to multi-agent settings by incorporating the notion of agent models into the state space. Agents maintain beliefs over physical states of the environment and over models of other agents, and they use Bayesian updates to maintain their beliefs over time. The solutions map belief states to actions. Models of other agents may include their belief states and are related to agent types considered in games of incomplete information. We express the agents autonomy by postulating that their models are not directly manipulable or observable by other agents. We show that important properties of POMDPs, such as convergence of value iteration, the rate of convergence, and piece-wise linearity and convexity of the value functions carry over to our framework. Our approach complements a more traditional approach to interactive settings which uses Nash equilibria as a solution paradigm. We seek to avoid some of the drawbacks of equilibria which may be non-unique and do not capture off-equilibrium behaviors. We do so at the cost of having to represent, process and continuously revise models of other agents. Since the agents beliefs may be arbitrarily nested, the optimal solutions to decision making problems are only asymptotically computable. However, approximate belief updates and approximately optimal plans are computable. We illustrate our framework using a simple application domain, and we show examples of belief updates and value functions.