Computational Cognitive Modeling, the Source of Power, and Other Related Issues

AI Magazine

In computational cognitive modeling, we hypothesize internal mental processes of human cognitive activities and express such activities by computer programs. Such computational models often consist of many components and aspects. Claims are often made that certain aspects play a key role in modeling, but such claims are sometimes not well justified or explored. In this article, we first review some fundamental distinctions and issues in computational modeling. We then discuss, in principle, systematic ways of identifying the source of power in models.

Scalable Models for Patterns of Life

AAAI Conferences

Patterns of life (POL) are emergent properties of complex social systems. Computational models of POL offer significant potential for practical application and theoretical study, but also important challenges for AI research. Computational POL models must achieve simultaneous scalability along three key dimensions: population size, intelligence, and automatic behavior specification. Three broad research areas that could support important improvements in POL modeling are pattern recognition, representational abstraction, and behavior generation with intelligent agents and the like. This paper describes challenges in POL modeling that AI researchers from many fields can help to meet.

Computational Urban Modeling: From Mainframes to Data Streams

AAAI Conferences

Assuming computational technologies as a dominant factor in forming new scientific methods during the last century, we review the field of computational urban modeling based on the ways different approaches deal with evolving computational and informational capacities. We claim that during the last few years, due to advancements in ubiquitous computing the flow of unstructured data streams have changed the landscape of empirical modeling and simulation. However, there is a conceptual mismatch between the state of the art in urban modeling paradigms and the capacities offered by these urban data streams. We discuss some alternative mathematical methodologies that introduce an abstraction from the traditional urban modeling methodologies.


AAAI Conferences

Introduction Recently, there has been an emergence of interest in understanding how observations of the actions of agents can be used as the basis for inference of the unobservable state of these agents, in order to improve the ability of the observer to respond to these agents. In particular, there is increasing interest in the area of Agent Modeling, which investigates mechanisms allowing an agent to acquire, maintain, and infer knowledge of other agents. This area unites plan-, goal-, and intent-recognition under a single umbrella with user-modeling, behaviorrecognition, belief ascription, agent tracking, etc. Traditionally, agent modeling researchers have explored techniques in which two agents are involved e.g., (Kautz & Allen 1986; Charniak & Goldman 1993; Lesh, Rich, & Sidner 1999). In such techniques one agent observes the actions of another agent, and attempts to infer its unobservable state features, such as intent, goal, or plan. These techniques are successful in many cases, and new techniques are still being investigated, e.g., (Pynadath 8z Wellman 2000). However, the transition from agent-modeling techniques, where an observing agent is monitoring the state of another agent, to multi-agent modeling, where the observing agent is monitoring the actions of more than one agent, present new challenges that have not been previously addressed by agent modeling researchers. These include both computational challenges, such as bandwidth and computational load, as well as conceptual challenges, such as reasoning about previously unseen behavior of teams of agents. This extended abstract outlines some of these current key challenges in multi-agent modeling, and the steps we have begun to take in address these challenges, specifically in the context of agents that are collaborating with each other. We focus on the following key challenges: The Monitoring Selectivity Challenge: Given finite bandwidth and computation resources, how can an *This research was carried out while the author was visiting Carnegie Mellon University.