Pierangelo Dell'Acqua

Preferential theory revision

Employing a logic program approach, this paper focuses on applying preferential reasoning to theory revision, both by means of preferences among existing theory rules, and by means of preferences on the possible abductive extensions to the theory. And, in particular, how to prefer among plausible abductive explanations justifying observations.

Reflection Principles in Computational Logic

We introduce the concept of reflection principles as a knowledge representation paradigm in a computational logic setting. Reflection principles are expressed as certain kinds of logic schemata int ...

Combining Introspection and Communication with Rationality and Reactivity in Agents

We propose a logic-based language for programming agents that can reason about their own beliefs as well as the beliefs of other agents and can communicate with each other. The agents can be reactive, rational/deliberative or hybrid, combining both reactive and rational behaviour. We illustrate the language by means of examples.

A Compilation of Updates plus Preferences

We show how to compile programs formalizing update plus preference reasoning into standard generalized logic programs and show the correctness of the transformation.

Emotional behavior trees

Often the AI techniques for decision making used in commercial games are predictable and unadaptive. Arguably, this causes a lack of realism for the players. We believe that emotions are a vital part in the creation of interesting and believable non-player characters for games. In this paper, we present an extension to behavior trees that incorporates emotions into the decision making. Specifically, we introduce a new type of priority selector whose priorities are dynamically evaluated to allow for emotional influence. This selector takes into account time-discounting, risk perception, and planning as relevant factors of the decision making that emotions greatly influence. The objective of our work is to provide game developers with a technique to model gaming scenarios using emotional behavior trees.

A logic based asynchronous multi-agent system

We present a logic programming based asynchronous multi-agent system in which agents can communicate with one another; update themselves and each other; abduce hypotheses to explain observations, and use them to generate actions. The knowledge base of the agents is comprised of generalized logic programs, integrity constraints, active rules, and of abducibles. We characterize the interaction among agents via an asynchronous transition rule system, and provide a stable models based semantics. An example is developed to illustrate how our approach works.

Reflective Agents in Metalogic Programming

We introduce a representation of agents by means of theories, and a communication among agents based on reflection, within the metalogic programming paradigm. The semantics of these features is shown to be the classical semantics of Horn clauses. The primitives for agents representation and inter-agent communication are very simple, and non-committal w.r.t. any predefined cognitive model or linguistic modality. Yet, it is shown by means of examples that they have enough expressive power for reasoning in non-trivial multi-agent domains, like the three wise men problem, especially when embedded in a powerful language, such as Reflective Prolog that we have previously developed. By integrating agents into Reflective Prolog we get a metalogic language equipped with higher-order-like features, metalevel negation, and theories, all of which rely on logical reflection for a uniform semantics and support each other for greater expressive and problem-solving power.

Preferring and Updating in Abductive Multi-agent Systems

We present a logical framework and the declarative semantics of a multi-agent system in which each agent can communicate with and update other agents, can react to the environment, is able to prefer, whether beliefs or reactions, when several alternatives are possible, and is able to abduce hypotheses to explain observations. The knowledge state of an agent is represented by an updatable prioritized abductive logic program, in which priorities among rules can be expressed to allow the agent to prefer. We sketch two examples to illustrate how our approach functions, including how to prefer abducibles to tackle the problem of multiple hypotheses and how to perform the interplay between planning and acting.We argue that the theory of the type of agents considered is a rich evolvable basis, and suitable for engineering configurable, dynamic, self-organizing and self-evolving agent societies.

On Preferring and Inspecting Abductive Models

This work proposes the application of preferences over abductive logic programs as an appealing declarative formalism to model choice situations. In particular, both a priori and a posteriori handling of preferences between abductive extensions of a theory are addressed as complementary and essential mechanisms in a broader framework for abductive reasoning. Furthermore, both of these choice mechanisms are combined with other formalisms for decision making, like economic decision theory, resulting in theories containing the best advantages from both qualitative and quantitative formalisms. Several examples are presented throughout to illustrate the enounced methodologies. These have been tested in our implementation, which we explain in detail.

Knowledge-based probability maps for covert pathfinding

Virtual characters in computer games sometimes need to find a path from point A to point B while minimizing the risk of being spotted by an enemy. Visibility calculations of the environment are needed to accomplish this. While previous methods have focused on either general visibility calculations or calculations based only on current enemy positions, we suggest a method to incorporate the agents knowledge of previous enemy positions. By creating a probability distribution of the positions of the enemies and using this in the visibility calculation, we create a more accurate visibility map.